Overview

Brought to you by YData

Dataset statistics

Number of variables103
Number of observations69686
Missing cells53380
Missing cells (%)0.7%
Total size in memory54.8 MiB
Average record size in memory824.0 B

Variable types

Text38
Numeric60
Unsupported5

Alerts

P_monto_total_procesos has 15561 (22.3%) missing values Missing
P_cant_proveedores has 15561 (22.3%) missing values Missing
duracion_contrato_dias is highly skewed (γ1 = 245.6624201) Skewed
cant_total_bienes is highly skewed (γ1 = 130.0258894) Skewed
cant_total_servicios is highly skewed (γ1 = 60.63968939) Skewed
TOT_AGROPECUARIO is highly skewed (γ1 = 183.8589333) Skewed
TOT_ALIMENTOS is highly skewed (γ1 = 70.55014309) Skewed
TOT_ALQUILER is highly skewed (γ1 = 179.3148012) Skewed
TOT_ART_HOGAR is highly skewed (γ1 = 170.5406575) Skewed
TOT_BANCO is highly skewed (γ1 = 161.609925) Skewed
TOT_BAZAR is highly skewed (γ1 = 108.2964855) Skewed
TOT_CARPINTERIA is highly skewed (γ1 = 170.9736269) Skewed
TOT_CEREMONIAL is highly skewed (γ1 = 212.2867341) Skewed
TOT_CERRAJERIA is highly skewed (γ1 = 184.8811841) Skewed
TOT_CINE is highly skewed (γ1 = 169.30579) Skewed
TOT_COMBUSTIBLES is highly skewed (γ1 = 156.7853707) Skewed
TOT_CONCESION is highly skewed (γ1 = 263.184346) Skewed
TOT_CONSTRUCCION is highly skewed (γ1 = 37.62191625) Skewed
TOT_CULTURA is highly skewed (γ1 = 156.9721476) Skewed
TOT_ELECTRICIDAD is highly skewed (γ1 = 260.9940546) Skewed
TOT_LIMPIEZA is highly skewed (γ1 = 254.5017361) Skewed
TOT_MUEBLES is highly skewed (γ1 = 263.1251928) Skewed
TOT_EQUIPO_MILITAR is highly skewed (γ1 = 140.926785) Skewed
TOT_EQUIPOS is highly skewed (γ1 = 106.8613452) Skewed
TOT_FERRETERIA is highly skewed (γ1 = 153.1404747) Skewed
TOT_GASES_IND is highly skewed (γ1 = 141.8115574) Skewed
TOT_HERRAMIENTAS is highly skewed (γ1 = 66.9180266) Skewed
TOT_HERRERIA is highly skewed (γ1 = 186.059942) Skewed
TOT_IMPRENTA is highly skewed (γ1 = 107.4848171) Skewed
TOT_INDUMENTARIA is highly skewed (γ1 = 99.93471317) Skewed
TOT_INFORMATICA is highly skewed (γ1 = 181.3185083) Skewed
TOT_INMUEBLES is highly skewed (γ1 = 101.085019) Skewed
TOT_INSUMO_ARMAMENTO is highly skewed (γ1 = 68.89889433) Skewed
TOT_JOYERIA is highly skewed (γ1 = 151.4388882) Skewed
TOT_LIBRERIA is highly skewed (γ1 = 165.7058378) Skewed
TOT_MANTENIMIENTO is highly skewed (γ1 = 152.4666774) Skewed
TOT_METALES is highly skewed (γ1 = 168.8137058) Skewed
TOT_METALURGIA is highly skewed (γ1 = 187.8503009) Skewed
TOT_CONSTRUCCION.1 is highly skewed (γ1 = 37.62191625) Skewed
TOT_NAUTICA is highly skewed (γ1 = 139.8332653) Skewed
TOT_PINTURAS is highly skewed (γ1 = 263.1450999) Skewed
TOT_PROD_MEDICOS is highly skewed (γ1 = 141.1744252) Skewed
TOT_PROD_VETERINARIOS is highly skewed (γ1 = 107.7967211) Skewed
TOT_QUIMICOS is highly skewed (γ1 = 133.8360939) Skewed
TOT_REPUESTOS is highly skewed (γ1 = 156.7298713) Skewed
TOT_PLOMERIA is highly skewed (γ1 = 170.6935309) Skewed
TOT_SERV_PROFESIONAL is highly skewed (γ1 = 61.1866789) Skewed
TOT_SERV_NOTICIAS is highly skewed (γ1 = 186.0944746) Skewed
TOT_SERV_BASICOS is highly skewed (γ1 = 182.6082737) Skewed
TOT_TAPICERIA is highly skewed (γ1 = 109.6085885) Skewed
TOT_TRANSPORTE is highly skewed (γ1 = 107.282094) Skewed
TOT_PROD_DEPORTIVOS is highly skewed (γ1 = 189.3948061) Skewed
TOT_VIDRIERIA is highly skewed (γ1 = 193.3228802) Skewed
TOT_VIGILANCIA is highly skewed (γ1 = 159.2243653) Skewed
P_monto_total_procesos is highly skewed (γ1 = 53.20733228) Skewed
num_proceso has unique values Unique
año_publicacion is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_publicacion is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_inicio_consultas is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_final_consultas is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_acto_apertura is an unsupported type, check if it needs cleaning or further analysis Unsupported
cro_cant_dias_publicar has 60983 (87.5%) zeros Zeros
proveedores_participantes has 3353 (4.8%) zeros Zeros
ofertas_confirmadas has 6210 (8.9%) zeros Zeros
cant_total_bienes has 28295 (40.6%) zeros Zeros
cant_total_servicios has 38900 (55.8%) zeros Zeros
TOT_AGROPECUARIO has 68842 (98.8%) zeros Zeros
TOT_ALIMENTOS has 65814 (94.4%) zeros Zeros
TOT_ALQUILER has 66799 (95.9%) zeros Zeros
TOT_ART_HOGAR has 67858 (97.4%) zeros Zeros
TOT_BANCO has 67808 (97.3%) zeros Zeros
TOT_BAZAR has 67355 (96.7%) zeros Zeros
TOT_CARPINTERIA has 68239 (97.9%) zeros Zeros
TOT_CEREMONIAL has 68668 (98.5%) zeros Zeros
TOT_CERRAJERIA has 68295 (98.0%) zeros Zeros
TOT_CINE has 68925 (98.9%) zeros Zeros
TOT_COMBUSTIBLES has 67068 (96.2%) zeros Zeros
TOT_CONCESION has 69198 (99.3%) zeros Zeros
TOT_CONSTRUCCION has 65972 (94.7%) zeros Zeros
TOT_CULTURA has 69112 (99.2%) zeros Zeros
TOT_ELECTRICIDAD has 64085 (92.0%) zeros Zeros
TOT_LIMPIEZA has 65857 (94.5%) zeros Zeros
TOT_MUEBLES has 67397 (96.7%) zeros Zeros
TOT_EQUIPO_MILITAR has 69010 (99.0%) zeros Zeros
TOT_EQUIPOS has 61840 (88.7%) zeros Zeros
TOT_FERRETERIA has 62097 (89.1%) zeros Zeros
TOT_GASES_IND has 69235 (99.4%) zeros Zeros
TOT_HERRAMIENTAS has 65732 (94.3%) zeros Zeros
TOT_HERRERIA has 68546 (98.4%) zeros Zeros
TOT_IMPRENTA has 68003 (97.6%) zeros Zeros
TOT_INDUMENTARIA has 63906 (91.7%) zeros Zeros
TOT_INFORMATICA has 65456 (93.9%) zeros Zeros
TOT_INMUEBLES has 69240 (99.4%) zeros Zeros
TOT_INSUMO_ARMAMENTO has 69176 (99.3%) zeros Zeros
TOT_JOYERIA has 69122 (99.2%) zeros Zeros
TOT_LIBRERIA has 63260 (90.8%) zeros Zeros
TOT_MANTENIMIENTO has 56847 (81.6%) zeros Zeros
TOT_METALES has 68788 (98.7%) zeros Zeros
TOT_METALURGIA has 68975 (99.0%) zeros Zeros
TOT_CONSTRUCCION.1 has 65972 (94.7%) zeros Zeros
TOT_NAUTICA has 68833 (98.8%) zeros Zeros
TOT_PINTURAS has 66380 (95.3%) zeros Zeros
TOT_PROD_MEDICOS has 63179 (90.7%) zeros Zeros
TOT_PROD_VETERINARIOS has 68901 (98.9%) zeros Zeros
TOT_QUIMICOS has 64593 (92.7%) zeros Zeros
TOT_REPUESTOS has 60184 (86.4%) zeros Zeros
TOT_PLOMERIA has 67287 (96.6%) zeros Zeros
TOT_SERV_PROFESIONAL has 56623 (81.3%) zeros Zeros
TOT_SERV_NOTICIAS has 69000 (99.0%) zeros Zeros
TOT_SERV_BASICOS has 67930 (97.5%) zeros Zeros
TOT_TAPICERIA has 69080 (99.1%) zeros Zeros
TOT_TRANSPORTE has 68091 (97.7%) zeros Zeros
TOT_PROD_DEPORTIVOS has 69097 (99.2%) zeros Zeros
TOT_VIDRIERIA has 68963 (99.0%) zeros Zeros
TOT_VIGILANCIA has 68552 (98.4%) zeros Zeros

Reproduction

Analysis started2025-05-28 00:29:27.797292
Analysis finished2025-05-28 00:29:47.869168
Duration20.07 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

num_proceso
Text

Unique 

Distinct69686
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:29:48.524420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length14.62890681
Min length13

Characters and Unicode

Total characters1019430
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69686 ?
Unique (%)100.0%

Sample

1st row23-0009-LPR16
2nd row23-0010-LPR16
3rd row23-0011-LPR16
4th row23-0012-LPR16
5th row23-0014-LPU16
ValueCountFrequency (%)
23-0017-lpu16 1
 
< 0.1%
98-0027-cdi22 1
 
< 0.1%
23-0009-lpr16 1
 
< 0.1%
23-0010-lpr16 1
 
< 0.1%
23-0011-lpr16 1
 
< 0.1%
23-0012-lpr16 1
 
< 0.1%
87-0001-cdi22 1
 
< 0.1%
87-0054-cdi22 1
 
< 0.1%
87-0056-cdi22 1
 
< 0.1%
87-0075-cdi22 1
 
< 0.1%
Other values (69676) 69676
> 99.9%
2025-05-27T21:29:49.804330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1019430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1019430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1019430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143911
14.1%
- 139372
13.7%
1 103427
 
10.1%
2 96191
 
9.4%
4 59231
 
5.8%
8 49679
 
4.9%
3 48466
 
4.8%
/ 38020
 
3.7%
C 37817
 
3.7%
D 37318
 
3.7%
Other values (12) 265998
26.1%
Distinct66201
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:29:50.769996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length43
Median length41
Mean length33.60598973
Min length29

Characters and Unicode

Total characters2341867
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62977 ?
Unique (%)90.4%

Sample

1st rowEX-2016-00697885- -APN-DPYS#SGP
2nd rowEX-2016-01031683- -APN-DPYS#SGP
3rd rowEX-2016-01358346- -APN-DDMYA#SGP
4th rowEX-2016-01392005- -APN-DDMYA#SGP
5th rowEX-2016-00474707- -APN-DPYS#SGP
ValueCountFrequency (%)
apn-dcon#faa 1991
 
1.4%
apn-dgit#ara 1423
 
1.0%
apn-dcyc#mc 1095
 
0.8%
apn-dacmysg#anlis 1004
 
0.7%
apn-dc#hp 946
 
0.7%
apn-dcyc#mds 827
 
0.6%
apn-gaen#cnea 800
 
0.6%
apn-da#inidep 791
 
0.6%
apn-dmza#dnv 787
 
0.6%
apn-gasnya#cnea 776
 
0.6%
Other values (66773) 128932
92.5%
2025-05-27T21:29:52.476643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2341867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 348430
14.9%
209058
 
8.9%
2 181300
 
7.7%
A 159361
 
6.8%
0 141609
 
6.0%
N 112020
 
4.8%
1 109636
 
4.7%
E 101456
 
4.3%
P 85268
 
3.6%
X 70457
 
3.0%
Other values (27) 823272
35.2%
Distinct60794
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:29:53.537524image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length253
Median length133
Mean length62.62734265
Min length5

Characters and Unicode

Total characters4364249
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56308 ?
Unique (%)80.8%

Sample

1st rowAdquisición de elementos de electricidad
2nd rowAdquisición de elementos de plomería y cerrajería.
3rd rowADQUISICIÓN INSUMOS PARA BAÑOS
4th rowServicio anual de mantenimiento, y controles mensuales de Extintores, y adquisición
5th rowAdquisición de indumentaria.
ValueCountFrequency (%)
de 109302
 
17.0%
para 27722
 
4.3%
y 25932
 
4.0%
adquisición 24358
 
3.8%
servicio 16245
 
2.5%
la 12477
 
1.9%
del 11279
 
1.8%
el 10265
 
1.6%
7475
 
1.2%
mantenimiento 6952
 
1.1%
Other values (25911) 389171
60.7%
2025-05-27T21:29:55.776212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4364249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4364249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4364249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
574961
 
13.2%
A 273739
 
6.3%
E 265215
 
6.1%
I 244218
 
5.6%
R 162998
 
3.7%
e 160977
 
3.7%
O 158236
 
3.6%
S 158146
 
3.6%
N 149918
 
3.4%
i 149004
 
3.4%
Other values (124) 2066837
47.4%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:29:56.466894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length19.03964928
Min length15

Characters and Unicode

Total characters1326797
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLicitación Privada
2nd rowLicitación Privada
3rd rowLicitación Privada
4th rowLicitación Privada
5th rowLicitación Pública
ValueCountFrequency (%)
contratación 37296
26.8%
directa 37296
26.8%
licitación 31474
22.6%
privada 22944
16.5%
pública 8925
 
6.4%
concurso 493
 
0.4%
subasta 395
 
0.3%
privado 274
 
0.2%
público 219
 
0.2%
compulsa 28
 
< 0.1%
Other values (2) 56
 
< 0.1%
2025-05-27T21:29:57.502203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1326797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1326797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1326797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 201404
15.2%
a 199267
15.0%
c 147205
11.1%
t 143757
10.8%
n 106559
8.0%
r 98331
7.4%
69714
 
5.3%
ó 68770
 
5.2%
o 38831
 
2.9%
C 37817
 
2.9%
Other values (14) 215142
16.2%
Distinct15397
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:29:58.419572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters1463406
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5611 ?
Unique (%)8.1%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:30 Hrs.
4th row30/11/2016 16:31 Hrs.
5th row27/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69686
33.3%
10:00 18690
 
8.9%
12:00 11030
 
5.3%
11:00 10445
 
5.0%
09:00 8590
 
4.1%
08:00 3907
 
1.9%
13:00 3816
 
1.8%
15:00 2640
 
1.3%
16:00 1855
 
0.9%
14:00 1583
 
0.8%
Other values (1666) 76816
36.7%
2025-05-27T21:30:00.000237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1463406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1463406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1463406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 335592
22.9%
2 176297
12.0%
1 170661
11.7%
139372
9.5%
/ 139372
9.5%
: 69686
 
4.8%
H 69686
 
4.8%
s 69686
 
4.8%
r 69686
 
4.8%
. 69686
 
4.8%
Other values (7) 153682
10.5%

estado
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:00.647257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length10
Mean length11.30017507
Min length8

Characters and Unicode

Total characters787464
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdjudicado
2nd rowDesierto
3rd rowAdjudicado
4th rowAdjudicado
5th rowAdjudicado
ValueCountFrequency (%)
adjudicado 54562
56.5%
dejado 13428
 
13.9%
sin 13428
 
13.9%
efecto 13428
 
13.9%
desierto 1696
 
1.8%
2025-05-27T21:30:01.443759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 787464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 787464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 787464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 177114
22.5%
o 83114
10.6%
i 69686
 
8.8%
a 67990
 
8.6%
c 67990
 
8.6%
j 67990
 
8.6%
A 54562
 
6.9%
u 54562
 
6.9%
e 30248
 
3.8%
26856
 
3.4%
Other values (8) 87352
11.1%
Distinct482
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:02.256596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length149
Median length84
Mean length44.9071693
Min length13

Characters and Unicode

Total characters3129401
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row23/000 - Dirección General de Administración - SG
2nd row23/000 - Dirección General de Administración - SG
3rd row23/000 - Dirección General de Administración - SG
4th row23/000 - Dirección General de Administración - SG
5th row23/000 - Dirección General de Administración - SG
ValueCountFrequency (%)
104265
 
20.0%
de 47301
 
9.1%
dirección 20429
 
3.9%
general 17242
 
3.3%
y 15439
 
3.0%
administración 13784
 
2.6%
compras 12303
 
2.4%
contrataciones 10812
 
2.1%
dnv 8451
 
1.6%
departamento 5389
 
1.0%
Other values (1182) 266717
51.1%
2025-05-27T21:30:03.778301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3129401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3129401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3129401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
453929
 
14.5%
i 193235
 
6.2%
a 190725
 
6.1%
e 186334
 
6.0%
n 167868
 
5.4%
r 143356
 
4.6%
- 111551
 
3.6%
o 106816
 
3.4%
c 105310
 
3.4%
t 98436
 
3.1%
Other values (74) 1371841
43.8%
Distinct144
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:04.646781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length102
Median length84
Mean length43.07286973
Min length25

Characters and Unicode

Total characters3001576
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row301 - Secretaria General de la Presidencia de la Nación
2nd row301 - Secretaria General de la Presidencia de la Nación
3rd row301 - Secretaria General de la Presidencia de la Nación
4th row301 - Secretaria General de la Presidencia de la Nación
5th row301 - Secretaria General de la Presidencia de la Nación
ValueCountFrequency (%)
69731
 
13.7%
de 58890
 
11.6%
nacional 28106
 
5.5%
general 22383
 
4.4%
estado 21663
 
4.3%
mayor 21284
 
4.2%
la 16595
 
3.3%
del 11499
 
2.3%
dirección 9519
 
1.9%
ejercito 8823
 
1.7%
Other values (387) 239352
47.1%
2025-05-27T21:30:06.293987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3001576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3001576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3001576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
438530
14.6%
a 293750
 
9.8%
e 243999
 
8.1%
i 201606
 
6.7%
r 169424
 
5.6%
o 162653
 
5.4%
d 150922
 
5.0%
n 142650
 
4.8%
l 122113
 
4.1%
c 104714
 
3.5%
Other values (64) 971215
32.4%

Unnamed: 0.1
Real number (ℝ)

Distinct1352
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.35604
Minimum0
Maximum1351
Zeros237
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:06.823404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q198
median237
Q3534
95-th percentile983
Maximum1351
Range1351
Interquartile range (IQR)436

Descriptive statistics

Standard deviation309.4564471
Coefficient of variation (CV)0.8986525898
Kurtosis0.04774300882
Mean344.35604
Median Absolute Deviation (MAD)173
Skewness1.003110159
Sum23996795
Variance95763.29266
MonotonicityNot monotonic
2025-05-27T21:30:07.221995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 237
 
0.3%
1 237
 
0.3%
2 236
 
0.3%
3 236
 
0.3%
4 235
 
0.3%
5 234
 
0.3%
6 234
 
0.3%
7 232
 
0.3%
8 230
 
0.3%
9 229
 
0.3%
Other values (1342) 67346
96.6%
ValueCountFrequency (%)
0 237
0.3%
1 237
0.3%
2 236
0.3%
3 236
0.3%
4 235
0.3%
ValueCountFrequency (%)
1351 1
< 0.1%
1350 1
< 0.1%
1349 1
< 0.1%
1348 1
< 0.1%
1347 1
< 0.1%

etapa
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:07.745337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.001937261
Min length5

Characters and Unicode

Total characters348565
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÚnica
2nd rowÚnica
3rd rowÚnica
4th rowÚnica
5th rowÚnica
ValueCountFrequency (%)
única 69612
99.9%
múltiple 45
 
0.1%
otros 29
 
< 0.1%
2025-05-27T21:30:08.661957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:09.449260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length13
Mean length14.69008122
Min length13

Characters and Unicode

Total characters1023693
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSin modalidad
2nd rowSin modalidad
3rd rowOrden de compra abierta
4th rowSin modalidad
5th rowSin modalidad
ValueCountFrequency (%)
sin 59263
36.3%
modalidad 59263
36.3%
de 10302
 
6.3%
compra 10260
 
6.3%
orden 10247
 
6.3%
abierta 10247
 
6.3%
889
 
0.5%
llave 858
 
0.5%
en 858
 
0.5%
mano 858
 
0.5%
Other values (15) 406
 
0.2%
2025-05-27T21:30:10.776386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1023693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 198374
19.4%
a 151211
14.8%
i 129180
12.6%
93765
9.2%
n 71387
 
7.0%
o 70587
 
6.9%
m 70407
 
6.9%
l 60296
 
5.9%
S 59424
 
5.8%
e 32930
 
3.2%
Other values (24) 86132
8.4%

moneda
Text

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:11.261674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length87
Median length14
Mean length15.16666188
Min length4

Characters and Unicode

Total characters1056904
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowPeso Argentino
2nd rowPeso Argentino
3rd rowPeso Argentino
4th rowPeso Argentino
5th rowPeso Argentino
ValueCountFrequency (%)
peso 67267
45.7%
argentino 67267
45.7%
dolar 4476
 
3.0%
estadounidense 4476
 
3.0%
euro 749
 
0.5%
749
 
0.5%
european 749
 
0.5%
monetary 749
 
0.5%
union 749
 
0.5%
sin 16
 
< 0.1%
Other values (4) 46
 
< 0.1%
2025-05-27T21:30:12.117198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1056904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1056904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1056904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 146508
13.9%
o 146498
13.9%
e 145004
13.7%
77607
7.3%
s 76261
7.2%
r 74010
7.0%
i 72528
6.9%
t 72518
6.9%
P 67267
6.4%
g 67267
6.4%
Other values (15) 111436
10.5%
Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:12.659331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length144
Median length64
Mean length65.87726373
Min length25

Characters and Unicode

Total characters4590723
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowDecreto Delegado N° 1023/2001 Art. 25
2nd rowDecreto Delegado N° 1023/2001 Art. 25
3rd rowDecreto Delegado N° 1023/2001 Art. 25
4th rowDecreto Delegado N° 1023/2001 Art. 25
5th rowDecreto Delegado N° 1023/2001 Art. 25
ValueCountFrequency (%)
decreto 143019
22.1%
art 76967
11.9%
25 76505
11.8%
n°1030/2016 75332
11.6%
67147
10.4%
delegado 66878
10.3%
1023/2001 66878
10.3%
art.14 35114
 
5.4%
art.12 22340
 
3.5%
art.10 8251
 
1.3%
Other values (56) 8875
 
1.4%
2025-05-27T21:30:13.757971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4590723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
577620
12.6%
0 437880
 
9.5%
e 423660
 
9.2%
1 353638
 
7.7%
2 309827
 
6.7%
r 287072
 
6.3%
t 287069
 
6.3%
o 212281
 
4.6%
D 210956
 
4.6%
N 144875
 
3.2%
Other values (51) 1345845
29.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:14.293702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters2787440
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo admite cotización parcial por renglón
2nd rowSe admite cotización parcial por renglón
3rd rowNo admite cotización parcial por renglón
4th rowNo admite cotización parcial por renglón
5th rowSe admite cotización parcial por renglón
ValueCountFrequency (%)
admite 69686
16.7%
cotización 69686
16.7%
parcial 69686
16.7%
renglón 69686
16.7%
por 69686
16.7%
no 57499
13.8%
se 12187
 
2.9%
2025-05-27T21:30:15.409678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2787440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
348430
12.5%
a 278744
10.0%
i 278744
10.0%
n 209058
 
7.5%
r 209058
 
7.5%
c 209058
 
7.5%
o 196871
 
7.1%
e 151559
 
5.4%
l 139372
 
5.0%
t 139372
 
5.0%
Other values (8) 627174
22.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:16.013969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.80142353
Min length7

Characters and Unicode

Total characters1031452
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrden de compra
2nd rowOrden de compra
3rd rowOrden de compra
4th rowOrden de compra
5th rowOrden de compra
ValueCountFrequency (%)
orden 67773
33.0%
de 67773
33.0%
compra 67341
32.8%
contrato 1898
 
0.9%
venta 432
 
0.2%
acuerdo 15
 
< 0.1%
2025-05-27T21:30:17.096947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1031452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 137027
13.3%
e 135993
13.2%
d 135561
13.1%
135546
13.1%
o 71152
6.9%
n 70103
6.8%
a 69671
6.8%
O 67773
6.6%
c 67356
6.5%
m 67341
6.5%
Other values (6) 73929
7.2%
Distinct8245
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:18.065778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length255
Median length238
Mean length42.8725282
Min length1

Characters and Unicode

Total characters2987615
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6314 ?
Unique (%)9.1%

Sample

1st row25 de Mayo 658, 4° piso.
2nd row25 de Mayo 658, 4° piso.
3rd row25 de Mayo 658, 4° piso.
4th row25 de Mayo 658, 4° piso.
5th row25 de Mayo 658, 4° piso.
ValueCountFrequency (%)
31108
 
5.6%
de 25265
 
4.6%
av 22886
 
4.1%
piso 19224
 
3.5%
caba 10440
 
1.9%
aires 8894
 
1.6%
buenos 8718
 
1.6%
y 8068
 
1.5%
7160
 
1.3%
oficina 6994
 
1.3%
Other values (4991) 403589
73.1%
2025-05-27T21:30:19.510245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2987615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2987615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2987615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
482795
 
16.2%
A 156301
 
5.2%
a 148928
 
5.0%
e 123429
 
4.1%
o 117774
 
3.9%
i 104708
 
3.5%
r 85189
 
2.9%
n 83200
 
2.8%
C 70445
 
2.4%
s 65902
 
2.2%
Other values (102) 1548944
51.8%
Distinct69
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:20.190743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length60
Median length33
Mean length32.98913699
Min length23

Characters and Unicode

Total characters2298881
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st row60 Días corridos Acto de apertura
2nd row60 Días corridos Acto de apertura
3rd row60 Días corridos Acto de apertura
4th row60 Días corridos Perfeccionamiento del documento contractual
5th row60 Días corridos Acto de apertura
ValueCountFrequency (%)
de 69657
16.7%
días 69566
16.6%
apertura 69505
16.6%
acto 69505
16.6%
60 66064
15.8%
corridos 64534
15.4%
hábiles 5032
 
1.2%
90 1333
 
0.3%
30 935
 
0.2%
120 559
 
0.1%
Other values (40) 1563
 
0.4%
2025-05-27T21:30:21.186989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2298881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
348567
15.2%
r 268364
11.7%
a 209119
9.1%
o 199107
 
8.7%
e 145169
 
6.3%
t 139506
 
6.1%
s 139439
 
6.1%
c 134792
 
5.9%
d 134401
 
5.8%
i 70080
 
3.0%
Other values (30) 510337
22.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:21.462274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 69575
99.8%
111
 
0.2%
2025-05-27T21:30:22.090885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69575
49.9%
o 69575
49.9%
S 111
 
0.1%
í 111
 
0.1%
Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:22.694082image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length84
Median length83
Mean length27.87126539
Min length9

Characters and Unicode

Total characters1942237
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos
ValueCountFrequency (%)
apartado 37296
12.6%
por 32842
11.1%
datos 32390
11.0%
sin 32390
11.0%
abreviada 26422
9.0%
compulsa 26422
9.0%
monto 25302
8.6%
1 25205
8.5%
adjudicación 10853
 
3.7%
simple 10853
 
3.7%
Other values (52) 35237
11.9%
2025-05-27T21:30:23.808489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1942237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1942237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1942237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
225526
 
11.6%
a 223269
 
11.5%
o 189130
 
9.7%
d 133919
 
6.9%
i 116129
 
6.0%
r 109917
 
5.7%
t 105592
 
5.4%
s 105051
 
5.4%
p 83418
 
4.3%
n 81777
 
4.2%
Other values (41) 568509
29.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:24.194551image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.001937261
Min length5

Characters and Unicode

Total characters348565
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowÚnica
2nd rowÚnica
3rd rowÚnica
4th rowÚnica
5th rowÚnica
ValueCountFrequency (%)
única 69612
99.9%
múltiple 45
 
0.1%
otros 29
 
< 0.1%
2025-05-27T21:30:25.040186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 69657
20.0%
Ú 69612
20.0%
n 69612
20.0%
c 69612
20.0%
a 69612
20.0%
l 90
 
< 0.1%
t 74
 
< 0.1%
ú 45
 
< 0.1%
M 45
 
< 0.1%
p 45
 
< 0.1%
Other values (5) 161
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:25.539186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length9
Mean length14.39660764
Min length9

Characters and Unicode

Total characters1003242
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos
ValueCountFrequency (%)
sin 42824
25.8%
datos 42824
25.8%
autorización 26862
16.2%
del 26862
16.2%
pliego 26862
16.2%
2025-05-27T21:30:26.376949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1003242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1003242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1003242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 123410
12.3%
96548
9.6%
o 96548
9.6%
s 85648
 
8.5%
d 69686
 
6.9%
n 69686
 
6.9%
a 69686
 
6.9%
t 69686
 
6.9%
e 53724
 
5.4%
l 53724
 
5.4%
Other values (8) 214896
21.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:26.883174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length9
Mean length14.38575898
Min length9

Characters and Unicode

Total characters1002486
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos
ValueCountFrequency (%)
sin 42878
25.8%
datos 42878
25.8%
autorización 26808
16.1%
de 26808
16.1%
llamado 26808
16.1%
2025-05-27T21:30:27.778226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1002486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1002486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1002486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 123302
12.3%
o 96494
9.6%
i 96494
9.6%
d 96494
9.6%
96494
9.6%
s 85756
8.6%
n 69686
 
7.0%
t 69686
 
7.0%
l 53616
 
5.3%
A 26808
 
2.7%
Other values (7) 187656
18.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:28.244346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length16
Mean length12.65700428
Min length9

Characters and Unicode

Total characters882016
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th rowsin datos
5th rowsin datos
ValueCountFrequency (%)
acto 36406
20.7%
de 36406
20.7%
apertura 36406
20.7%
sin 33280
18.9%
datos 33280
18.9%
2025-05-27T21:30:29.197657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
106092
12.0%
t 106092
12.0%
a 106092
12.0%
e 72812
8.3%
r 72812
8.3%
o 69686
7.9%
d 69686
7.9%
s 66560
7.5%
A 36406
 
4.1%
c 36406
 
4.1%
Other values (4) 139372
15.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:29.437421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 69055
99.1%
si 631
 
0.9%
2025-05-27T21:30:30.062262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69055
49.5%
o 69055
49.5%
S 631
 
0.5%
i 631
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:30.285233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters139372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 69500
99.7%
si 186
 
0.3%
2025-05-27T21:30:30.918235image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 69500
49.9%
o 69500
49.9%
S 186
 
0.1%
i 186
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing410
Missing (%)0.6%
Memory size544.5 KiB
2025-05-27T21:30:31.203185image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters138552
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 50932
73.5%
18344
 
26.5%
2025-05-27T21:30:31.787390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 50932
36.8%
o 50932
36.8%
S 18344
 
13.2%
í 18344
 
13.2%
Distinct25992
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:32.557885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.98742933
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13788 ?
Unique (%)19.8%

Sample

1st row21/09/2016 18:01 Hrs.
2nd row02/09/2016 09:00 Hrs.
3rd row07/12/2016 18:30 Hrs.
4th row15/11/2016 16:30 Hrs.
5th row21/09/2016 15:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
08:00 13835
 
6.6%
10:00 8170
 
3.9%
09:00 6357
 
3.0%
12:00 4536
 
2.2%
14:00 3518
 
1.7%
15:00 3119
 
1.5%
13:00 3043
 
1.5%
11:00 2776
 
1.3%
16:00 2401
 
1.1%
Other values (2008) 91617
43.8%
2025-05-27T21:30:33.757043image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 329823
22.6%
2 172272
11.8%
1 151010
10.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 182689
12.5%
Distinct31333
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:34.567108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.98742933
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19336 ?
Unique (%)27.7%

Sample

1st row21/09/2016 18:02 Hrs.
2nd row02/09/2016 10:00 Hrs.
3rd row07/12/2016 19:00 Hrs.
4th row15/11/2016 16:31 Hrs.
5th row21/09/2016 15:01 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
10:00 9076
 
4.3%
08:00 8662
 
4.1%
09:00 8095
 
3.9%
11:00 4286
 
2.1%
12:00 2796
 
1.3%
08:30 1837
 
0.9%
08:01 1819
 
0.9%
13:00 1811
 
0.9%
15:00 1774
 
0.8%
Other values (2101) 99216
47.5%
2025-05-27T21:30:35.797673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 317704
21.7%
2 170698
11.7%
1 161686
11.1%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 185706
12.7%
Distinct19810
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:36.702198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.98742933
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7106 ?
Unique (%)10.2%

Sample

1st row06/10/2016 13:01 Hrs.
2nd row07/09/2016 11:00 Hrs.
3rd row19/12/2016 12:00 Hrs.
4th row24/11/2016 16:31 Hrs.
5th row21/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
12:00 11635
 
5.6%
10:00 10560
 
5.1%
13:00 6186
 
3.0%
16:00 6012
 
2.9%
09:00 4407
 
2.1%
11:00 4370
 
2.1%
17:00 4140
 
2.0%
15:00 3592
 
1.7%
18:00 3590
 
1.7%
Other values (1867) 84880
40.6%
2025-05-27T21:30:38.051244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 318315
21.8%
2 179841
12.3%
1 165212
11.3%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
r 69613
 
4.8%
. 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 172426
11.8%

cro_cant_dias_publicar
Real number (ℝ)

Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2535803461
Minimum0
Maximum30
Zeros60983
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:38.437423image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7217048461
Coefficient of variation (CV)2.846059867
Kurtosis104.7307897
Mean0.2535803461
Median Absolute Deviation (MAD)0
Skewness5.495287953
Sum17671
Variance0.5208578848
MonotonicityNot monotonic
2025-05-27T21:30:38.910871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 60983
87.5%
2 8401
 
12.1%
1 211
 
0.3%
3 24
 
< 0.1%
5 22
 
< 0.1%
10 16
 
< 0.1%
8 9
 
< 0.1%
7 8
 
< 0.1%
6 2
 
< 0.1%
20 2
 
< 0.1%
Other values (8) 8
 
< 0.1%
ValueCountFrequency (%)
0 60983
87.5%
1 211
 
0.3%
2 8401
 
12.1%
3 24
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
30 1
< 0.1%
23 1
< 0.1%
22 1
< 0.1%
20 2
< 0.1%
17 1
< 0.1%
Distinct13987
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:39.964682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length9
Mean length14.18221738
Min length9

Characters and Unicode

Total characters988302
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8245 ?
Unique (%)11.8%

Sample

1st row27/09/2016 18:02 Hrs.
2nd row02/09/2016 09:00 Hrs.
3rd row12/12/2016 10:00 Hrs.
4th row30/11/2016 16:30 Hrs.
5th row26/09/2016 15:01 Hrs.
ValueCountFrequency (%)
sin 39592
23.4%
datos 39592
23.4%
hrs 30094
17.8%
10:00 5724
 
3.4%
08:00 5569
 
3.3%
09:00 3753
 
2.2%
11:00 2574
 
1.5%
12:00 1735
 
1.0%
07:00 942
 
0.6%
08:30 744
 
0.4%
Other values (1893) 39147
23.1%
2025-05-27T21:30:41.450146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 143624
14.5%
s 109278
 
11.1%
99780
 
10.1%
2 72808
 
7.4%
1 67634
 
6.8%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 276622
28.0%
Distinct13763
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:42.764669image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length9
Mean length14.18221738
Min length9

Characters and Unicode

Total characters988302
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7541 ?
Unique (%)10.8%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:00 Hrs.
4th row30/11/2016 16:30 Hrs.
5th row27/10/2016 11:59 Hrs.
ValueCountFrequency (%)
sin 39592
23.4%
datos 39592
23.4%
hrs 30094
17.8%
12:00 5482
 
3.2%
10:00 4924
 
2.9%
11:00 4252
 
2.5%
09:00 2882
 
1.7%
08:00 1645
 
1.0%
13:00 1519
 
0.9%
16:00 1012
 
0.6%
Other values (1696) 38472
22.7%
2025-05-27T21:30:44.530921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 988302
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 136427
13.8%
s 109278
 
11.1%
99780
 
10.1%
2 76910
 
7.8%
1 73680
 
7.5%
/ 60188
 
6.1%
i 39592
 
4.0%
t 39592
 
4.0%
a 39592
 
4.0%
d 39592
 
4.0%
Other values (13) 273671
27.7%
Distinct15392
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:45.744055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length21
Mean length20.98742933
Min length9

Characters and Unicode

Total characters1462530
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5610 ?
Unique (%)8.1%

Sample

1st row13/10/2016 13:02 Hrs.
2nd row13/09/2016 11:00 Hrs.
3rd row19/12/2016 12:30 Hrs.
4th row30/11/2016 16:31 Hrs.
5th row27/10/2016 12:00 Hrs.
ValueCountFrequency (%)
hrs 69613
33.3%
10:00 18670
 
8.9%
12:00 11014
 
5.3%
11:00 10436
 
5.0%
09:00 8586
 
4.1%
08:00 3906
 
1.9%
13:00 3804
 
1.8%
15:00 2637
 
1.3%
16:00 1855
 
0.9%
14:00 1582
 
0.8%
Other values (1668) 76882
36.8%
2025-05-27T21:30:47.208829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1462530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 335254
22.9%
2 176177
12.0%
1 170475
11.7%
139299
9.5%
/ 139226
9.5%
s 69759
 
4.8%
: 69613
 
4.8%
. 69613
 
4.8%
r 69613
 
4.8%
H 69613
 
4.8%
Other values (13) 153888
10.5%
Distinct230
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:47.730778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length88
Median length56
Mean length62.56493413
Min length9

Characters and Unicode

Total characters4359900
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)0.1%

Sample

1st rowA partir del perfeccionamiento del documento contractual
2nd rowDentro de los 20 Días corridos del perfeccionamiento del documento contractual
3rd rowA partir del perfeccionamiento del documento contractual
4th rowA partir del perfeccionamiento del documento contractual
5th rowA partir del perfeccionamiento del documento contractual
ValueCountFrequency (%)
del 140364
24.2%
perfeccionamiento 69668
12.0%
contractual 69668
12.0%
documento 69668
12.0%
a 54447
 
9.4%
partir 45130
 
7.8%
los 24538
 
4.2%
días 24538
 
4.2%
de 15221
 
2.6%
dentro 15221
 
2.6%
Other values (110) 51168
 
8.8%
2025-05-27T21:30:48.848852image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4359900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4359900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4359900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
509945
11.7%
e 467468
10.7%
o 407381
9.3%
c 357972
 
8.2%
t 340069
 
7.8%
n 294939
 
6.8%
a 278690
 
6.4%
r 264081
 
6.1%
l 249476
 
5.7%
d 234903
 
5.4%
Other values (23) 954976
21.9%
Distinct328
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:49.426860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.01917171
Min length5

Characters and Unicode

Total characters837568
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.1%

Sample

1st row5 Días hábiles
2nd row30 Días corridos
3rd row12 Meses
4th row12 Meses
5th row120 Días corridos
ValueCountFrequency (%)
días 38418
21.6%
meses 29590
16.6%
corridos 19659
11.0%
hábiles 18759
10.5%
12 18310
10.3%
15 9256
 
5.2%
30 7963
 
4.5%
6 4404
 
2.5%
10 4089
 
2.3%
60 3661
 
2.1%
Other values (195) 24049
13.5%
2025-05-27T21:30:51.246851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 837568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 837568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 837568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 137429
16.4%
108472
13.0%
e 78331
 
9.4%
o 40512
 
4.8%
r 39594
 
4.7%
a 39196
 
4.7%
i 39196
 
4.7%
í 38786
 
4.6%
D 38786
 
4.6%
1 35762
 
4.3%
Other values (20) 241504
28.8%

proveedores_participantes
Real number (ℝ)

Zeros 

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.933257756
Minimum0
Maximum319
Zeros3353
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:51.710430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum319
Range319
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.237789104
Coefficient of variation (CV)1.061730273
Kurtosis200.341758
Mean4.933257756
Median Absolute Deviation (MAD)2
Skewness5.456080964
Sum343779
Variance27.4344347
MonotonicityNot monotonic
2025-05-27T21:30:52.205254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17559
25.2%
2 8052
11.6%
3 6995
 
10.0%
4 5894
 
8.5%
5 4993
 
7.2%
6 4245
 
6.1%
7 3391
 
4.9%
0 3353
 
4.8%
8 2845
 
4.1%
9 2308
 
3.3%
Other values (53) 10051
14.4%
ValueCountFrequency (%)
0 3353
 
4.8%
1 17559
25.2%
2 8052
11.6%
3 6995
 
10.0%
4 5894
 
8.5%
ValueCountFrequency (%)
319 1
< 0.1%
139 1
< 0.1%
99 1
< 0.1%
74 1
< 0.1%
65 2
< 0.1%

ofertas_confirmadas
Real number (ℝ)

Zeros 

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.331673507
Minimum0
Maximum105
Zeros6210
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:52.622777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile10
Maximum105
Range105
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.550355339
Coefficient of variation (CV)1.065637234
Kurtosis21.29156465
Mean3.331673507
Median Absolute Deviation (MAD)1
Skewness2.774199082
Sum232171
Variance12.60502304
MonotonicityNot monotonic
2025-05-27T21:30:53.039212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 22248
31.9%
2 10032
14.4%
3 7694
 
11.0%
0 6210
 
8.9%
4 5764
 
8.3%
5 4434
 
6.4%
6 3336
 
4.8%
7 2540
 
3.6%
8 1833
 
2.6%
9 1312
 
1.9%
Other values (34) 4283
 
6.1%
ValueCountFrequency (%)
0 6210
 
8.9%
1 22248
31.9%
2 10032
14.4%
3 7694
 
11.0%
4 5764
 
8.3%
ValueCountFrequency (%)
105 1
 
< 0.1%
78 1
 
< 0.1%
53 1
 
< 0.1%
45 1
 
< 0.1%
42 3
< 0.1%

año_publicacion
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size544.5 KiB

año_apertura
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.004162
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:53.404668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2017
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.523312861
Coefficient of variation (CV)0.000754113724
Kurtosis-0.9236189233
Mean2020.004162
Median Absolute Deviation (MAD)1
Skewness-0.3188607987
Sum140766010
Variance2.320482072
MonotonicityIncreasing
2025-05-27T21:30:53.755270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 16171
23.2%
2022 14210
20.4%
2019 13381
19.2%
2020 12628
18.1%
2018 9212
13.2%
2017 3840
 
5.5%
2016 244
 
0.4%
ValueCountFrequency (%)
2016 244
 
0.4%
2017 3840
 
5.5%
2018 9212
13.2%
2019 13381
19.2%
2020 12628
18.1%
ValueCountFrequency (%)
2022 14210
20.4%
2021 16171
23.2%
2020 12628
18.1%
2019 13381
19.2%
2018 9212
13.2%

periodo_apertura
Real number (ℝ)

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202007.5722
Minimum201608
Maximum202212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:54.229791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum201608
5-th percentile201711
Q1201905
median202009
Q3202110
95-th percentile202209
Maximum202212
Range604
Interquartile range (IQR)205

Descriptive statistics

Standard deviation151.8909695
Coefficient of variation (CV)0.0007519073066
Kurtosis-0.9302531568
Mean202007.5722
Median Absolute Deviation (MAD)102
Skewness-0.3173464218
Sum1.407709968 × 1010
Variance23070.86662
MonotonicityNot monotonic
2025-05-27T21:30:54.742024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202006 1896
 
2.7%
202107 1693
 
2.4%
202111 1679
 
2.4%
202108 1627
 
2.3%
202205 1580
 
2.3%
202109 1543
 
2.2%
202208 1535
 
2.2%
201908 1528
 
2.2%
202105 1523
 
2.2%
201909 1516
 
2.2%
Other values (67) 53566
76.9%
ValueCountFrequency (%)
201608 3
 
< 0.1%
201609 22
 
< 0.1%
201610 35
 
0.1%
201611 81
0.1%
201612 103
0.1%
ValueCountFrequency (%)
202212 577
 
0.8%
202211 1353
1.9%
202210 956
1.4%
202209 1326
1.9%
202208 1535
2.2%

periodo_publicacion
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size544.5 KiB

periodo_inicio_consultas
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size544.5 KiB

periodo_final_consultas
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size544.5 KiB
Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:55.737700image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.704445656
Min length6

Characters and Unicode

Total characters536892
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201609
2nd row201609
3rd row201612
4th row201611
5th row201609
ValueCountFrequency (%)
sin 39592
36.2%
datos 39592
36.2%
202107 714
 
0.7%
202204 680
 
0.6%
202208 668
 
0.6%
201905 652
 
0.6%
201908 642
 
0.6%
202111 638
 
0.6%
202205 613
 
0.6%
202106 599
 
0.5%
Other values (69) 24888
22.8%
2025-05-27T21:30:56.868654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 60404
11.3%
2 56983
10.6%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63177
11.8%
Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:57.726942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.704445656
Min length6

Characters and Unicode

Total characters536892
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201610
2nd row201609
3rd row201612
4th row201611
5th row201610
ValueCountFrequency (%)
sin 39592
36.2%
datos 39592
36.2%
202205 704
 
0.6%
201905 695
 
0.6%
202107 686
 
0.6%
202208 658
 
0.6%
201908 638
 
0.6%
202007 633
 
0.6%
202111 620
 
0.6%
202109 609
 
0.6%
Other values (69) 24851
22.7%
2025-05-27T21:30:59.079474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 536892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 79184
14.7%
0 59964
11.2%
2 57535
10.7%
i 39592
7.4%
n 39592
7.4%
39592
7.4%
a 39592
7.4%
d 39592
7.4%
o 39592
7.4%
t 39592
7.4%
Other values (8) 63065
11.7%

periodo_acto_apertura
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size544.5 KiB
Distinct126
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:30:59.825978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length6.595758115
Min length3

Characters and Unicode

Total characters459632
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.1%

Sample

1st row16.0
2nd row11.0
3rd row7.0
4th row0.0
5th row31.0
ValueCountFrequency (%)
sin 39592
36.2%
datos 39592
36.2%
7.0 3429
 
3.1%
8.0 2793
 
2.6%
0.0 2347
 
2.1%
6.0 1936
 
1.8%
9.0 1875
 
1.7%
10.0 1753
 
1.6%
14.0 1591
 
1.5%
11.0 1560
 
1.4%
Other values (117) 12810
 
11.7%
2025-05-27T21:31:01.079388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 79184
17.2%
i 39592
8.6%
n 39592
8.6%
39592
8.6%
d 39592
8.6%
a 39592
8.6%
t 39592
8.6%
o 39592
8.6%
0 34664
7.5%
. 30094
 
6.5%
Other values (9) 38546
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 459632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 79184
17.2%
i 39592
8.6%
n 39592
8.6%
39592
8.6%
d 39592
8.6%
a 39592
8.6%
t 39592
8.6%
o 39592
8.6%
0 34664
7.5%
. 30094
 
6.5%
Other values (9) 38546
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 459632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 79184
17.2%
i 39592
8.6%
n 39592
8.6%
39592
8.6%
d 39592
8.6%
a 39592
8.6%
t 39592
8.6%
o 39592
8.6%
0 34664
7.5%
. 30094
 
6.5%
Other values (9) 38546
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 459632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 79184
17.2%
i 39592
8.6%
n 39592
8.6%
39592
8.6%
d 39592
8.6%
a 39592
8.6%
t 39592
8.6%
o 39592
8.6%
0 34664
7.5%
. 30094
 
6.5%
Other values (9) 38546
8.4%

duracion_contrato_dias
Real number (ℝ)

Skewed 

Distinct242
Distinct (%)0.3%
Missing410
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean182.160272
Minimum1
Maximum364635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:01.569883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q128
median90
Q3360
95-th percentile360
Maximum364635
Range364634
Interquartile range (IQR)332

Descriptive statistics

Standard deviation1417.610637
Coefficient of variation (CV)7.782216299
Kurtosis63070.62468
Mean182.160272
Median Absolute Deviation (MAD)75
Skewness245.6624201
Sum12619335
Variance2009619.919
MonotonicityNot monotonic
2025-05-27T21:31:02.118065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 18031
25.9%
21 5587
 
8.0%
30 4941
 
7.1%
180 4876
 
7.0%
90 3955
 
5.7%
15 3658
 
5.2%
42 3404
 
4.9%
60 2871
 
4.1%
14 2828
 
4.1%
28 1910
 
2.7%
Other values (232) 17215
24.7%
ValueCountFrequency (%)
1 138
 
0.2%
2 245
0.4%
3 75
 
0.1%
4 14
 
< 0.1%
5 374
0.5%
ValueCountFrequency (%)
364635 1
 
< 0.1%
36135 1
 
< 0.1%
18250 2
< 0.1%
12600 1
 
< 0.1%
10950 4
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing18
Missing (%)< 0.1%
Memory size544.5 KiB
2025-05-27T21:31:02.801101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length92
Median length56
Mean length62.84611299
Min length56

Characters and Unicode

Total characters4378363
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA partir del perfeccionamiento del documento contractual
2nd rowEntre 16 y 30 Días del perfeccionamiento del documento contractual
3rd rowA partir del perfeccionamiento del documento contractual
4th rowA partir del perfeccionamiento del documento contractual
5th rowA partir del perfeccionamiento del documento contractual
ValueCountFrequency (%)
del 148653
25.4%
perfeccionamiento 69668
11.9%
documento 69668
11.9%
contractual 69668
11.9%
partir 45130
 
7.7%
a 45130
 
7.7%
días 24538
 
4.2%
entre 15864
 
2.7%
10 13804
 
2.4%
los 11959
 
2.0%
Other values (12) 70972
12.1%
2025-05-27T21:31:03.767103image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
515386
11.8%
e 493618
11.3%
o 372258
 
8.5%
t 353470
 
8.1%
c 348340
 
8.0%
n 308340
 
7.0%
a 278672
 
6.4%
r 264891
 
6.1%
l 230280
 
5.3%
d 226995
 
5.2%
Other values (20) 986113
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4378363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
515386
11.8%
e 493618
11.3%
o 372258
 
8.5%
t 353470
 
8.1%
c 348340
 
8.0%
n 308340
 
7.0%
a 278672
 
6.4%
r 264891
 
6.1%
l 230280
 
5.3%
d 226995
 
5.2%
Other values (20) 986113
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4378363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
515386
11.8%
e 493618
11.3%
o 372258
 
8.5%
t 353470
 
8.1%
c 348340
 
8.0%
n 308340
 
7.0%
a 278672
 
6.4%
r 264891
 
6.1%
l 230280
 
5.3%
d 226995
 
5.2%
Other values (20) 986113
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4378363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
515386
11.8%
e 493618
11.3%
o 372258
 
8.5%
t 353470
 
8.1%
c 348340
 
8.0%
n 308340
 
7.0%
a 278672
 
6.4%
r 264891
 
6.1%
l 230280
 
5.3%
d 226995
 
5.2%
Other values (20) 986113
22.5%

cant_total_bienes
Real number (ℝ)

Skewed  Zeros 

Distinct8490
Distinct (%)12.3%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean84589.63978
Minimum0
Maximum650697097
Zeros28295
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:04.174337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3493.75
95-th percentile26197.5
Maximum650697097
Range650697097
Interquartile range (IQR)493.75

Descriptive statistics

Standard deviation3947828.919
Coefficient of variation (CV)46.67035975
Kurtosis19529.41221
Mean84589.63978
Median Absolute Deviation (MAD)5
Skewness130.0258894
Sum5859185989
Variance1.558535318 × 1013
MonotonicityNot monotonic
2025-05-27T21:31:04.704104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28295
40.6%
1 3348
 
4.8%
2 1299
 
1.9%
3 767
 
1.1%
4 722
 
1.0%
6 523
 
0.8%
10 470
 
0.7%
5 459
 
0.7%
12 406
 
0.6%
8 384
 
0.6%
Other values (8480) 32593
46.8%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 28295
40.6%
1 3348
 
4.8%
2 1299
 
1.9%
3 767
 
1.1%
4 722
 
1.0%
ValueCountFrequency (%)
650697097 1
< 0.1%
607177029 1
< 0.1%
291024000 1
< 0.1%
270136939 1
< 0.1%
136350000 1
< 0.1%

cant_total_servicios
Real number (ℝ)

Skewed  Zeros 

Distinct1993
Distinct (%)2.9%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean77380.75653
Minimum0
Maximum275762175
Zeros38900
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:05.207688image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile278
Maximum275762175
Range275762175
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2771725.92
Coefficient of variation (CV)35.81931793
Kurtosis4483.750254
Mean77380.75653
Median Absolute Deviation (MAD)0
Skewness60.63968939
Sum5359855482
Variance7.682464573 × 1012
MonotonicityNot monotonic
2025-05-27T21:31:05.771045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38900
55.8%
1 9713
 
13.9%
12 4475
 
6.4%
2 1900
 
2.7%
24 1208
 
1.7%
3 908
 
1.3%
6 764
 
1.1%
4 703
 
1.0%
36 572
 
0.8%
5 403
 
0.6%
Other values (1983) 9720
 
13.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 38900
55.8%
1 9713
 
13.9%
2 1900
 
2.7%
3 908
 
1.3%
4 703
 
1.0%
ValueCountFrequency (%)
275762175 1
 
< 0.1%
255000000 1
 
< 0.1%
230314200 1
 
< 0.1%
210000000 1
 
< 0.1%
144000000 3
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:06.266041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length4
Mean length5.67796975
Min length4

Characters and Unicode

Total characters395675
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBIEN
2nd rowBIEN
3rd rowBIEN
4th rowMIXTO
5th rowBIEN
ValueCountFrequency (%)
bien 38900
55.8%
servicio 28715
41.2%
mixto 2071
 
3.0%
2025-05-27T21:31:07.065488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 98401
24.9%
E 67615
17.1%
B 38900
 
9.8%
N 38900
 
9.8%
O 30786
 
7.8%
S 28715
 
7.3%
V 28715
 
7.3%
R 28715
 
7.3%
C 28715
 
7.3%
M 2071
 
0.5%
Other values (2) 4142
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 395675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 98401
24.9%
E 67615
17.1%
B 38900
 
9.8%
N 38900
 
9.8%
O 30786
 
7.8%
S 28715
 
7.3%
V 28715
 
7.3%
R 28715
 
7.3%
C 28715
 
7.3%
M 2071
 
0.5%
Other values (2) 4142
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 395675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 98401
24.9%
E 67615
17.1%
B 38900
 
9.8%
N 38900
 
9.8%
O 30786
 
7.8%
S 28715
 
7.3%
V 28715
 
7.3%
R 28715
 
7.3%
C 28715
 
7.3%
M 2071
 
0.5%
Other values (2) 4142
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 395675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 98401
24.9%
E 67615
17.1%
B 38900
 
9.8%
N 38900
 
9.8%
O 30786
 
7.8%
S 28715
 
7.3%
V 28715
 
7.3%
R 28715
 
7.3%
C 28715
 
7.3%
M 2071
 
0.5%
Other values (2) 4142
 
1.0%

TOT_AGROPECUARIO
Real number (ℝ)

Skewed  Zeros 

Distinct105
Distinct (%)0.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean10.14653654
Minimum0
Maximum234421
Zeros68842
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:07.447738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum234421
Range234421
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1040.072555
Coefficient of variation (CV)102.5051801
Kurtosis38762.09047
Mean10.14653654
Median Absolute Deviation (MAD)0
Skewness183.8589333
Sum702810
Variance1081750.921
MonotonicityNot monotonic
2025-05-27T21:31:07.913179image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68842
98.8%
1 136
 
0.2%
2 37
 
0.1%
12 29
 
< 0.1%
3 22
 
< 0.1%
5 18
 
< 0.1%
4 16
 
< 0.1%
10 13
 
< 0.1%
40 8
 
< 0.1%
20 7
 
< 0.1%
Other values (95) 138
 
0.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68842
98.8%
1 136
 
0.2%
2 37
 
0.1%
3 22
 
< 0.1%
4 16
 
< 0.1%
ValueCountFrequency (%)
234421 1
< 0.1%
96000 1
< 0.1%
76832 1
< 0.1%
27960 1
< 0.1%
26050 1
< 0.1%

TOT_ALIMENTOS
Real number (ℝ)

Skewed  Zeros 

Distinct1988
Distinct (%)2.9%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean14341.35849
Minimum0
Maximum38400000
Zeros65814
Zeros (%)94.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:08.396262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum38400000
Range38400000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation368311.4348
Coefficient of variation (CV)25.68176754
Kurtosis6207.022722
Mean14341.35849
Median Absolute Deviation (MAD)0
Skewness70.55014309
Sum993368537
Variance1.35653313 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:08.899834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65814
94.4%
1 47
 
0.1%
1000 28
 
< 0.1%
1200000 28
 
< 0.1%
600 26
 
< 0.1%
3000 25
 
< 0.1%
12 24
 
< 0.1%
70000 24
 
< 0.1%
1500 23
 
< 0.1%
300 21
 
< 0.1%
Other values (1978) 3206
 
4.6%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 65814
94.4%
1 47
 
0.1%
2 19
 
< 0.1%
3 5
 
< 0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
38400000 2
< 0.1%
33000000 2
< 0.1%
27428392 1
 
< 0.1%
20000000 3
< 0.1%
18305735 1
 
< 0.1%

TOT_ALQUILER
Real number (ℝ)

Skewed  Zeros 

Distinct245
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean435.8920394
Minimum0
Maximum10303920
Zeros66799
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:09.456493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10303920
Range10303920
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49783.89753
Coefficient of variation (CV)114.2115318
Kurtosis34158.91973
Mean435.8920394
Median Absolute Deviation (MAD)0
Skewness179.3148012
Sum30192498
Variance2478436453
MonotonicityNot monotonic
2025-05-27T21:31:09.947664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66799
95.9%
12 552
 
0.8%
24 443
 
0.6%
36 315
 
0.5%
1 181
 
0.3%
60 108
 
0.2%
48 86
 
0.1%
6 74
 
0.1%
2 41
 
0.1%
3 39
 
0.1%
Other values (235) 628
 
0.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 66799
95.9%
1 181
 
0.3%
2 41
 
0.1%
3 39
 
0.1%
4 34
 
< 0.1%
ValueCountFrequency (%)
10303920 1
< 0.1%
7554240 1
< 0.1%
1200000 1
< 0.1%
1136436 1
< 0.1%
1080000 1
< 0.1%

TOT_ART_HOGAR
Real number (ℝ)

Skewed  Zeros 

Distinct188
Distinct (%)0.3%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean23.23616204
Minimum0
Maximum538776
Zeros67858
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:10.395266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum538776
Range538776
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2673.301057
Coefficient of variation (CV)115.0491657
Kurtosis31253.14839
Mean23.23616204
Median Absolute Deviation (MAD)0
Skewness170.5406575
Sum1609476
Variance7146538.54
MonotonicityNot monotonic
2025-05-27T21:31:10.903338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67858
97.4%
1 180
 
0.3%
2 156
 
0.2%
4 95
 
0.1%
3 74
 
0.1%
10 72
 
0.1%
6 71
 
0.1%
5 61
 
0.1%
8 32
 
< 0.1%
20 28
 
< 0.1%
Other values (178) 639
 
0.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67858
97.4%
1 180
 
0.3%
2 156
 
0.2%
3 74
 
0.1%
4 95
 
0.1%
ValueCountFrequency (%)
538776 1
< 0.1%
400000 1
< 0.1%
153456 1
< 0.1%
110496 1
< 0.1%
60000 1
< 0.1%

TOT_BANCO
Real number (ℝ)

Skewed  Zeros 

Distinct126
Distinct (%)0.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5380.298054
Minimum0
Maximum144000000
Zeros67808
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:11.380292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum144000000
Range144000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation837009.9644
Coefficient of variation (CV)155.5694417
Kurtosis26755.15334
Mean5380.298054
Median Absolute Deviation (MAD)0
Skewness161.609925
Sum372671725
Variance7.005856805 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:11.912544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67808
97.3%
1 736
 
1.1%
2 160
 
0.2%
12 105
 
0.2%
3 64
 
0.1%
24 37
 
0.1%
10 37
 
0.1%
4 31
 
< 0.1%
5 24
 
< 0.1%
6 23
 
< 0.1%
Other values (116) 241
 
0.3%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67808
97.3%
1 736
 
1.1%
2 160
 
0.2%
3 64
 
0.1%
4 31
 
< 0.1%
ValueCountFrequency (%)
144000000 2
< 0.1%
84000000 1
< 0.1%
257000 1
< 0.1%
39144 1
< 0.1%
22568 1
< 0.1%

TOT_BAZAR
Real number (ℝ)

Skewed  Zeros 

Distinct566
Distinct (%)0.8%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean271.8615049
Minimum0
Maximum2612953
Zeros67355
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:12.529441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2612953
Range2612953
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17869.92908
Coefficient of variation (CV)65.73173752
Kurtosis13550.26048
Mean271.8615049
Median Absolute Deviation (MAD)0
Skewness108.2964855
Sum18830759
Variance319334365.5
MonotonicityNot monotonic
2025-05-27T21:31:13.115716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67355
96.7%
10 123
 
0.2%
2 64
 
0.1%
50 63
 
0.1%
100 62
 
0.1%
5 60
 
0.1%
20 58
 
0.1%
1 57
 
0.1%
3 46
 
0.1%
6 43
 
0.1%
Other values (556) 1335
 
1.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67355
96.7%
1 57
 
0.1%
2 64
 
0.1%
3 46
 
0.1%
4 40
 
0.1%
ValueCountFrequency (%)
2612953 1
< 0.1%
2480916 1
< 0.1%
1252702 1
< 0.1%
1240458 2
< 0.1%
1172872 1
< 0.1%

TOT_CARPINTERIA
Real number (ℝ)

Skewed  Zeros 

Distinct294
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean19.60156498
Minimum0
Maximum372000
Zeros68239
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:13.562509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum372000
Range372000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1744.155108
Coefficient of variation (CV)88.9804008
Kurtosis32931.75318
Mean19.60156498
Median Absolute Deviation (MAD)0
Skewness170.9736269
Sum1357722
Variance3042077.042
MonotonicityNot monotonic
2025-05-27T21:31:14.059960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68239
97.9%
1 72
 
0.1%
2 49
 
0.1%
4 35
 
0.1%
5 30
 
< 0.1%
20 30
 
< 0.1%
3 30
 
< 0.1%
10 28
 
< 0.1%
6 24
 
< 0.1%
100 19
 
< 0.1%
Other values (284) 710
 
1.0%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68239
97.9%
1 72
 
0.1%
2 49
 
0.1%
3 30
 
< 0.1%
4 35
 
0.1%
ValueCountFrequency (%)
372000 1
< 0.1%
186000 1
< 0.1%
165000 1
< 0.1%
70000 1
< 0.1%
23000 2
< 0.1%

TOT_CEREMONIAL
Real number (ℝ)

Skewed  Zeros 

Distinct259
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean23.48059654
Minimum0
Maximum707466
Zeros68668
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:14.583882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum707466
Range707466
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2961.765264
Coefficient of variation (CV)126.1367129
Kurtosis48527.00709
Mean23.48059654
Median Absolute Deviation (MAD)0
Skewness212.2867341
Sum1626407
Variance8772053.477
MonotonicityNot monotonic
2025-05-27T21:31:15.195027image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68668
98.5%
1 78
 
0.1%
10 15
 
< 0.1%
20 14
 
< 0.1%
4 13
 
< 0.1%
2 12
 
< 0.1%
30 11
 
< 0.1%
15 11
 
< 0.1%
100 9
 
< 0.1%
3 9
 
< 0.1%
Other values (249) 426
 
0.6%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68668
98.5%
1 78
 
0.1%
2 12
 
< 0.1%
3 9
 
< 0.1%
4 13
 
< 0.1%
ValueCountFrequency (%)
707466 1
< 0.1%
300000 1
< 0.1%
78400 1
< 0.1%
60000 1
< 0.1%
58220 1
< 0.1%

TOT_CERRAJERIA
Real number (ℝ)

Skewed  Zeros 

Distinct249
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean6.029682673
Minimum0
Maximum140700
Zeros68295
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:15.720731image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum140700
Range140700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation679.495957
Coefficient of variation (CV)112.6918271
Kurtosis35400.35733
Mean6.029682673
Median Absolute Deviation (MAD)0
Skewness184.8811841
Sum417652
Variance461714.7556
MonotonicityNot monotonic
2025-05-27T21:31:16.326159image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68295
98.0%
10 55
 
0.1%
20 48
 
0.1%
2 44
 
0.1%
5 41
 
0.1%
6 34
 
< 0.1%
3 30
 
< 0.1%
15 29
 
< 0.1%
4 27
 
< 0.1%
8 22
 
< 0.1%
Other values (239) 641
 
0.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68295
98.0%
1 18
 
< 0.1%
2 44
 
0.1%
3 30
 
< 0.1%
4 27
 
< 0.1%
ValueCountFrequency (%)
140700 1
< 0.1%
106932 1
< 0.1%
19250 1
< 0.1%
12890 1
< 0.1%
6260 1
< 0.1%

TOT_CINE
Real number (ℝ)

Skewed  Zeros 

Distinct68
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5281.966607
Minimum0
Maximum130000000
Zeros68925
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:16.804173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum130000000
Range130000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation724611.0288
Coefficient of variation (CV)137.1858406
Kurtosis30007.58757
Mean5281.966607
Median Absolute Deviation (MAD)0
Skewness169.30579
Sum365860699
Variance5.25061143 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:17.867288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68925
98.9%
1 212
 
0.3%
2 16
 
< 0.1%
12 12
 
< 0.1%
6 9
 
< 0.1%
10 7
 
< 0.1%
4 4
 
< 0.1%
5 4
 
< 0.1%
110 3
 
< 0.1%
50 3
 
< 0.1%
Other values (58) 71
 
0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68925
98.9%
1 212
 
0.3%
2 16
 
< 0.1%
3 3
 
< 0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
130000000 2
< 0.1%
33000000 1
< 0.1%
23000000 1
< 0.1%
22000000 1
< 0.1%
21000000 1
< 0.1%

TOT_COMBUSTIBLES
Real number (ℝ)

Skewed  Zeros 

Distinct510
Distinct (%)0.7%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4516.231571
Minimum0
Maximum90000000
Zeros67068
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:18.388716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum90000000
Range90000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation443625.0732
Coefficient of variation (CV)98.22903592
Kurtosis27892.25669
Mean4516.231571
Median Absolute Deviation (MAD)0
Skewness156.7853707
Sum312821296
Variance1.968032056 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:18.850137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67068
96.2%
10 137
 
0.2%
1 130
 
0.2%
2 127
 
0.2%
5 99
 
0.1%
4 92
 
0.1%
3 90
 
0.1%
20 80
 
0.1%
15 56
 
0.1%
6 52
 
0.1%
Other values (500) 1335
 
1.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67068
96.2%
1 130
 
0.2%
2 127
 
0.2%
3 90
 
0.1%
4 92
 
0.1%
ValueCountFrequency (%)
90000000 1
< 0.1%
45149559 1
< 0.1%
45000000 1
< 0.1%
30000000 1
< 0.1%
20000000 1
< 0.1%

TOT_CONCESION
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3325.080804
Minimum0
Maximum230314200
Zeros69198
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:19.165855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum230314200
Range230314200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation875105.9987
Coefficient of variation (CV)263.1833781
Kurtosis69266
Mean3325.080804
Median Absolute Deviation (MAD)0
Skewness263.184346
Sum230315047
Variance7.65810509 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:19.414887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 69198
99.3%
1 37
 
0.1%
2 15
 
< 0.1%
16 5
 
< 0.1%
230314200 1
 
< 0.1%
58 1
 
< 0.1%
6 1
 
< 0.1%
34 1
 
< 0.1%
118 1
 
< 0.1%
94 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69198
99.3%
1 37
 
0.1%
2 15
 
< 0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
230314200 1
< 0.1%
196 1
< 0.1%
118 1
< 0.1%
94 1
< 0.1%
90 1
< 0.1%

TOT_CONSTRUCCION
Real number (ℝ)

Skewed  Zeros 

Distinct1430
Distinct (%)2.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean279.0064534
Minimum0
Maximum360000
Zeros65972
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:19.809357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum360000
Range360000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4742.551285
Coefficient of variation (CV)16.99799853
Kurtosis1889.615957
Mean279.0064534
Median Absolute Deviation (MAD)0
Skewness37.62191625
Sum19325661
Variance22491792.69
MonotonicityNot monotonic
2025-05-27T21:31:20.280094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65972
94.7%
60 44
 
0.1%
200 41
 
0.1%
50 39
 
0.1%
20 36
 
0.1%
30 36
 
0.1%
80 35
 
0.1%
8 34
 
< 0.1%
6 32
 
< 0.1%
120 32
 
< 0.1%
Other values (1420) 2965
 
4.3%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 65972
94.7%
1 26
 
< 0.1%
2 14
 
< 0.1%
3 16
 
< 0.1%
4 23
 
< 0.1%
ValueCountFrequency (%)
360000 1
< 0.1%
317278 1
< 0.1%
309140 1
< 0.1%
252876 1
< 0.1%
244464 1
< 0.1%

TOT_CULTURA
Real number (ℝ)

Skewed  Zeros 

Distinct97
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean73.17603153
Minimum0
Maximum1690000
Zeros69112
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:20.771905image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1690000
Range1690000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9841.985367
Coefficient of variation (CV)134.4973916
Kurtosis25907.5564
Mean73.17603153
Median Absolute Deviation (MAD)0
Skewness156.9721476
Sum5068611
Variance96864675.97
MonotonicityNot monotonic
2025-05-27T21:31:21.296774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69112
99.2%
1 16
 
< 0.1%
2 6
 
< 0.1%
8 5
 
< 0.1%
3000 5
 
< 0.1%
10 5
 
< 0.1%
100 4
 
< 0.1%
300 4
 
< 0.1%
30 4
 
< 0.1%
34 3
 
< 0.1%
Other values (87) 102
 
0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69112
99.2%
1 16
 
< 0.1%
2 6
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
1690000 2
< 0.1%
800000 1
< 0.1%
584062 1
< 0.1%
117432 1
< 0.1%
34200 1
< 0.1%

TOT_ELECTRICIDAD
Real number (ℝ)

Skewed  Zeros 

Distinct1409
Distinct (%)2.0%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean137.2047325
Minimum0
Maximum4620000
Zeros64085
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:21.792680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum4620000
Range4620000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17603.86455
Coefficient of variation (CV)128.303625
Kurtosis68484.47373
Mean137.2047325
Median Absolute Deviation (MAD)0
Skewness260.9940546
Sum9503623
Variance309896046.9
MonotonicityNot monotonic
2025-05-27T21:31:22.361575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64085
92.0%
1 382
 
0.5%
2 200
 
0.3%
4 148
 
0.2%
10 140
 
0.2%
3 126
 
0.2%
20 98
 
0.1%
5 93
 
0.1%
6 80
 
0.1%
12 70
 
0.1%
Other values (1399) 3844
 
5.5%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 64085
92.0%
1 382
 
0.5%
2 200
 
0.3%
3 126
 
0.2%
4 148
 
0.2%
ValueCountFrequency (%)
4620000 1
< 0.1%
237574 1
< 0.1%
64170 1
< 0.1%
63575 1
< 0.1%
62749 1
< 0.1%

TOT_LIMPIEZA
Real number (ℝ)

Skewed  Zeros 

Distinct1480
Distinct (%)2.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean693.2717928
Minimum0
Maximum25000000
Zeros65857
Zeros (%)94.5%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:22.858817image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum25000000
Range25000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation96107.45591
Coefficient of variation (CV)138.6288277
Kurtosis66106.30528
Mean693.2717928
Median Absolute Deviation (MAD)0
Skewness254.5017361
Sum48020164
Variance9236643081
MonotonicityNot monotonic
2025-05-27T21:31:23.325244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65857
94.5%
2 108
 
0.2%
10 103
 
0.1%
20 96
 
0.1%
5 92
 
0.1%
1 80
 
0.1%
4 78
 
0.1%
30 59
 
0.1%
50 57
 
0.1%
100 46
 
0.1%
Other values (1470) 2690
 
3.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 65857
94.5%
1 80
 
0.1%
2 108
 
0.2%
3 45
 
0.1%
4 78
 
0.1%
ValueCountFrequency (%)
25000000 1
< 0.1%
2358150 1
< 0.1%
2000000 1
< 0.1%
1104000 1
< 0.1%
985248 1
< 0.1%

TOT_MUEBLES
Real number (ℝ)

Skewed  Zeros 

Distinct290
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean298.5227384
Minimum0
Maximum20000000
Zeros67397
Zeros (%)96.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:23.708904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20000000
Range20000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation75998.05012
Coefficient of variation (CV)254.5804401
Kurtosis69245.12838
Mean298.5227384
Median Absolute Deviation (MAD)0
Skewness263.1251928
Sum20677476
Variance5775703622
MonotonicityNot monotonic
2025-05-27T21:31:24.157137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67397
96.7%
1 329
 
0.5%
2 173
 
0.2%
3 104
 
0.1%
4 97
 
0.1%
5 81
 
0.1%
10 69
 
0.1%
6 64
 
0.1%
12 40
 
0.1%
20 38
 
0.1%
Other values (280) 874
 
1.3%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67397
96.7%
1 329
 
0.5%
2 173
 
0.2%
3 104
 
0.1%
4 97
 
0.1%
ValueCountFrequency (%)
20000000 1
< 0.1%
200000 1
< 0.1%
120000 1
< 0.1%
60375 1
< 0.1%
30000 1
< 0.1%

TOT_EQUIPO_MILITAR
Real number (ℝ)

Skewed  Zeros 

Distinct98
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.268761008
Minimum0
Maximum30000
Zeros69010
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:24.606023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30000
Range30000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation182.2620198
Coefficient of variation (CV)80.33548669
Kurtosis22044.89621
Mean2.268761008
Median Absolute Deviation (MAD)0
Skewness140.926785
Sum157148
Variance33219.44386
MonotonicityNot monotonic
2025-05-27T21:31:25.045956image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69010
99.0%
1 38
 
0.1%
2 32
 
< 0.1%
3 12
 
< 0.1%
4 12
 
< 0.1%
10 11
 
< 0.1%
6 8
 
< 0.1%
50 7
 
< 0.1%
5 7
 
< 0.1%
20 6
 
< 0.1%
Other values (88) 123
 
0.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69010
99.0%
1 38
 
0.1%
2 32
 
< 0.1%
3 12
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
30000 2
< 0.1%
15180 1
< 0.1%
10000 1
< 0.1%
5100 1
< 0.1%
5000 1
< 0.1%

TOT_EQUIPOS
Real number (ℝ)

Skewed  Zeros 

Distinct394
Distinct (%)0.6%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean27.45326712
Minimum0
Maximum252000
Zeros61840
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:25.465504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum252000
Range252000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1525.150989
Coefficient of variation (CV)55.55444396
Kurtosis14181.16749
Mean27.45326712
Median Absolute Deviation (MAD)0
Skewness106.8613452
Sum1901578
Variance2326085.54
MonotonicityNot monotonic
2025-05-27T21:31:25.905352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61840
88.7%
1 1883
 
2.7%
2 877
 
1.3%
3 525
 
0.8%
4 460
 
0.7%
5 293
 
0.4%
10 256
 
0.4%
6 239
 
0.3%
20 165
 
0.2%
8 161
 
0.2%
Other values (384) 2567
 
3.7%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 61840
88.7%
1 1883
 
2.7%
2 877
 
1.3%
3 525
 
0.8%
4 460
 
0.7%
ValueCountFrequency (%)
252000 1
< 0.1%
168989 1
< 0.1%
100050 1
< 0.1%
100000 2
< 0.1%
87000 1
< 0.1%

TOT_FERRETERIA
Real number (ℝ)

Skewed  Zeros 

Distinct1593
Distinct (%)2.3%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2124.111628
Minimum0
Maximum50000000
Zeros62097
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:26.484234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile91
Maximum50000000
Range50000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation249767.0508
Coefficient of variation (CV)117.5865936
Kurtosis26555.08403
Mean2124.111628
Median Absolute Deviation (MAD)0
Skewness153.1404747
Sum147128716
Variance6.238357965 × 1010
MonotonicityNot monotonic
2025-05-27T21:31:27.023857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62097
89.1%
10 249
 
0.4%
1 222
 
0.3%
2 193
 
0.3%
20 193
 
0.3%
5 160
 
0.2%
4 152
 
0.2%
3 149
 
0.2%
6 135
 
0.2%
50 132
 
0.2%
Other values (1583) 5584
 
8.0%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 62097
89.1%
1 222
 
0.3%
2 193
 
0.3%
3 149
 
0.2%
4 152
 
0.2%
ValueCountFrequency (%)
50000000 1
< 0.1%
25000000 1
< 0.1%
22000000 2
< 0.1%
15000000 1
< 0.1%
1175000 1
< 0.1%

TOT_GASES_IND
Real number (ℝ)

Skewed  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.01681921866
Minimum0
Maximum304
Zeros69235
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:27.390354image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum304
Range304
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.596572005
Coefficient of variation (CV)94.9254562
Kurtosis23357.8988
Mean0.01681921866
Median Absolute Deviation (MAD)0
Skewness141.8115574
Sum1165
Variance2.549042166
MonotonicityNot monotonic
2025-05-27T21:31:27.753345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 69235
99.4%
2 5
 
< 0.1%
6 3
 
< 0.1%
3 3
 
< 0.1%
10 3
 
< 0.1%
4 2
 
< 0.1%
40 2
 
< 0.1%
5 2
 
< 0.1%
100 2
 
< 0.1%
20 2
 
< 0.1%
Other values (7) 7
 
< 0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69235
99.4%
1 1
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
304 1
< 0.1%
200 1
< 0.1%
110 1
< 0.1%
100 2
< 0.1%
60 1
< 0.1%

TOT_HERRAMIENTAS
Real number (ℝ)

Skewed  Zeros 

Distinct336
Distinct (%)0.5%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4.167224901
Minimum0
Maximum10000
Zeros65732
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:28.254134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10000
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.0867183
Coefficient of variation (CV)24.01759461
Kurtosis5561.853589
Mean4.167224901
Median Absolute Deviation (MAD)0
Skewness66.9180266
Sum288647
Variance10017.35119
MonotonicityNot monotonic
2025-05-27T21:31:28.754898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65732
94.3%
1 400
 
0.6%
2 309
 
0.4%
4 181
 
0.3%
3 175
 
0.3%
10 170
 
0.2%
5 165
 
0.2%
20 110
 
0.2%
6 108
 
0.2%
8 85
 
0.1%
Other values (326) 1831
 
2.6%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 65732
94.3%
1 400
 
0.6%
2 309
 
0.4%
3 175
 
0.3%
4 181
 
0.3%
ValueCountFrequency (%)
10000 1
< 0.1%
9600 2
< 0.1%
9000 1
< 0.1%
6075 1
< 0.1%
6000 1
< 0.1%

TOT_HERRERIA
Real number (ℝ)

Skewed  Zeros 

Distinct146
Distinct (%)0.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean354.5432535
Minimum0
Maximum12000000
Zeros68546
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:29.215459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12000000
Range12000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64485.26465
Coefficient of variation (CV)181.8826448
Kurtosis34621.65788
Mean354.5432535
Median Absolute Deviation (MAD)0
Skewness186.059942
Sum24557793
Variance4158349357
MonotonicityNot monotonic
2025-05-27T21:31:29.703324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68546
98.4%
1 85
 
0.1%
2 80
 
0.1%
3 40
 
0.1%
4 38
 
0.1%
20 36
 
0.1%
10 31
 
< 0.1%
6 31
 
< 0.1%
30 21
 
< 0.1%
15 19
 
< 0.1%
Other values (136) 339
 
0.5%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68546
98.4%
1 85
 
0.1%
2 80
 
0.1%
3 40
 
0.1%
4 38
 
0.1%
ValueCountFrequency (%)
12000000 2
< 0.1%
120000 1
< 0.1%
80000 2
< 0.1%
60000 1
< 0.1%
54700 1
< 0.1%

TOT_IMPRENTA
Real number (ℝ)

Skewed  Zeros 

Distinct453
Distinct (%)0.7%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean9711.928349
Minimum0
Maximum91750000
Zeros68003
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:30.154040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum91750000
Range91750000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation561719.0166
Coefficient of variation (CV)57.83805197
Kurtosis14049.89483
Mean9711.928349
Median Absolute Deviation (MAD)0
Skewness107.4848171
Sum672706429
Variance3.155282536 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:30.508779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68003
97.6%
1 222
 
0.3%
2 35
 
0.1%
12 34
 
< 0.1%
500 26
 
< 0.1%
20 23
 
< 0.1%
1000 22
 
< 0.1%
10 21
 
< 0.1%
3 19
 
< 0.1%
100 19
 
< 0.1%
Other values (443) 842
 
1.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68003
97.6%
1 222
 
0.3%
2 35
 
0.1%
3 19
 
< 0.1%
4 16
 
< 0.1%
ValueCountFrequency (%)
91750000 1
< 0.1%
55905000 1
< 0.1%
51500438 1
< 0.1%
50500000 1
< 0.1%
30696571 1
< 0.1%

TOT_INDUMENTARIA
Real number (ℝ)

Skewed  Zeros 

Distinct1240
Distinct (%)1.8%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean236.1917391
Minimum0
Maximum1000000
Zeros63906
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:31.088985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile39
Maximum1000000
Range1000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6288.198719
Coefficient of variation (CV)26.62327964
Kurtosis13299.23099
Mean236.1917391
Median Absolute Deviation (MAD)0
Skewness99.93471317
Sum16360057
Variance39541443.13
MonotonicityNot monotonic
2025-05-27T21:31:31.577124image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63906
91.7%
10 213
 
0.3%
2 171
 
0.2%
20 161
 
0.2%
1 149
 
0.2%
4 111
 
0.2%
50 101
 
0.1%
3 90
 
0.1%
6 90
 
0.1%
30 88
 
0.1%
Other values (1230) 4186
 
6.0%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 63906
91.7%
1 149
 
0.2%
2 171
 
0.2%
3 90
 
0.1%
4 111
 
0.2%
ValueCountFrequency (%)
1000000 1
< 0.1%
780665 1
< 0.1%
402000 1
< 0.1%
324580 1
< 0.1%
300000 1
< 0.1%

TOT_INFORMATICA
Real number (ℝ)

Skewed  Zeros 

Distinct486
Distinct (%)0.7%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean165.2643577
Minimum0
Maximum4608000
Zeros65456
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:32.119941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4608000
Range4608000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21030.54472
Coefficient of variation (CV)127.2539646
Kurtosis36237.27243
Mean165.2643577
Median Absolute Deviation (MAD)0
Skewness181.3185083
Sum11447201
Variance442283811.3
MonotonicityNot monotonic
2025-05-27T21:31:32.586359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65456
93.9%
1 708
 
1.0%
2 314
 
0.5%
3 161
 
0.2%
4 129
 
0.2%
10 126
 
0.2%
5 108
 
0.2%
6 98
 
0.1%
20 91
 
0.1%
50 83
 
0.1%
Other values (476) 1992
 
2.9%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 65456
93.9%
1 708
 
1.0%
2 314
 
0.5%
3 161
 
0.2%
4 129
 
0.2%
ValueCountFrequency (%)
4608000 1
< 0.1%
2304000 1
< 0.1%
1850000 1
< 0.1%
740000 1
< 0.1%
209994 1
< 0.1%

TOT_INMUEBLES
Real number (ℝ)

Skewed  Zeros 

Distinct5
Distinct (%)< 0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.0004764242197
Minimum0
Maximum5
Zeros69240
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:32.935183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.02967235553
Coefficient of variation (CV)62.28137509
Kurtosis13866.75078
Mean0.0004764242197
Median Absolute Deviation (MAD)0
Skewness101.085019
Sum33
Variance0.0008804486826
MonotonicityNot monotonic
2025-05-27T21:31:33.236417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 69240
99.4%
1 23
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69240
99.4%
1 23
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
3 1
 
< 0.1%
2 1
 
< 0.1%
1 23
 
< 0.1%
0 69240
99.4%

TOT_INSUMO_ARMAMENTO
Real number (ℝ)

Skewed  Zeros 

Distinct67
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean860.3346086
Minimum0
Maximum5410000
Zeros69176
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:33.637222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5410000
Range5410000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51628.72547
Coefficient of variation (CV)60.01005301
Kurtosis5192.75925
Mean860.3346086
Median Absolute Deviation (MAD)0
Skewness68.89889433
Sum59591937
Variance2665525293
MonotonicityNot monotonic
2025-05-27T21:31:34.134478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69176
99.3%
1000000 3
 
< 0.1%
10000 3
 
< 0.1%
1 3
 
< 0.1%
30 3
 
< 0.1%
40 3
 
< 0.1%
750 2
 
< 0.1%
50 2
 
< 0.1%
45000 2
 
< 0.1%
9000 2
 
< 0.1%
Other values (57) 67
 
0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69176
99.3%
1 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5410000 1
< 0.1%
4196517 1
< 0.1%
4000000 1
< 0.1%
3767280 2
< 0.1%
3758800 1
< 0.1%

TOT_JOYERIA
Real number (ℝ)

Skewed  Zeros 

Distinct95
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.779069096
Minimum0
Maximum34095
Zeros69122
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:34.638218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum34095
Range34095
Interquartile range (IQR)0

Descriptive statistics

Standard deviation199.8721259
Coefficient of variation (CV)112.3464661
Kurtosis23521.84755
Mean1.779069096
Median Absolute Deviation (MAD)0
Skewness151.4388882
Sum123229
Variance39948.8667
MonotonicityNot monotonic
2025-05-27T21:31:35.140031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69122
99.2%
10 11
 
< 0.1%
25 4
 
< 0.1%
29 4
 
< 0.1%
150 4
 
< 0.1%
18 4
 
< 0.1%
23 3
 
< 0.1%
100 3
 
< 0.1%
8 3
 
< 0.1%
60 3
 
< 0.1%
Other values (85) 105
 
0.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69122
99.2%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
34095 1
< 0.1%
30147 1
< 0.1%
25500 1
< 0.1%
5000 1
< 0.1%
2500 1
< 0.1%

TOT_LIBRERIA
Real number (ℝ)

Skewed  Zeros 

Distinct2183
Distinct (%)3.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean7123.127595
Minimum0
Maximum136350000
Zeros63260
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:36.112795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile94
Maximum136350000
Range136350000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation649948.1472
Coefficient of variation (CV)91.24477113
Kurtosis31139.95879
Mean7123.127595
Median Absolute Deviation (MAD)0
Skewness165.7058378
Sum493390556
Variance4.22432594 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:36.641005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63260
90.8%
10 198
 
0.3%
1 184
 
0.3%
2 163
 
0.2%
20 145
 
0.2%
4 135
 
0.2%
5 126
 
0.2%
3 109
 
0.2%
6 99
 
0.1%
100 92
 
0.1%
Other values (2173) 4755
 
6.8%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 63260
90.8%
1 184
 
0.3%
2 163
 
0.2%
3 109
 
0.2%
4 135
 
0.2%
ValueCountFrequency (%)
136350000 1
< 0.1%
67580000 1
< 0.1%
64970000 1
< 0.1%
23754669 1
< 0.1%
20000000 1
< 0.1%

TOT_MANTENIMIENTO
Real number (ℝ)

Skewed  Zeros 

Distinct638
Distinct (%)0.9%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean14531.97003
Minimum0
Maximum275762175
Zeros56847
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:37.100775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14
Maximum275762175
Range275762175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1429259.626
Coefficient of variation (CV)98.35277826
Kurtosis25646.65396
Mean14531.97003
Median Absolute Deviation (MAD)0
Skewness152.4666774
Sum1006571436
Variance2.042783078 × 1012
MonotonicityNot monotonic
2025-05-27T21:31:37.524317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56847
81.6%
1 3756
 
5.4%
12 2067
 
3.0%
2 814
 
1.2%
3 457
 
0.7%
4 390
 
0.6%
24 380
 
0.5%
6 342
 
0.5%
5 261
 
0.4%
10 208
 
0.3%
Other values (628) 3744
 
5.4%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 56847
81.6%
1 3756
 
5.4%
2 814
 
1.2%
3 457
 
0.7%
4 390
 
0.6%
ValueCountFrequency (%)
275762175 1
< 0.1%
180000000 1
< 0.1%
154000000 1
< 0.1%
63600000 1
< 0.1%
34500000 1
< 0.1%

TOT_METALES
Real number (ℝ)

Skewed  Zeros 

Distinct112
Distinct (%)0.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.182008489
Minimum0
Maximum76000
Zeros68788
Zeros (%)98.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:37.943916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum76000
Range76000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation352.4061889
Coefficient of variation (CV)110.7496068
Kurtosis33029.66356
Mean3.182008489
Median Absolute Deviation (MAD)0
Skewness168.8137058
Sum220405
Variance124190.122
MonotonicityNot monotonic
2025-05-27T21:31:38.444362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68788
98.7%
10 33
 
< 0.1%
2 33
 
< 0.1%
1 23
 
< 0.1%
40 22
 
< 0.1%
50 21
 
< 0.1%
4 19
 
< 0.1%
6 19
 
< 0.1%
5 19
 
< 0.1%
30 18
 
< 0.1%
Other values (102) 271
 
0.4%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68788
98.7%
1 23
 
< 0.1%
2 33
 
< 0.1%
3 14
 
< 0.1%
4 19
 
< 0.1%
ValueCountFrequency (%)
76000 1
< 0.1%
30000 1
< 0.1%
25000 2
< 0.1%
24000 1
< 0.1%
9000 1
< 0.1%

TOT_METALURGIA
Real number (ℝ)

Skewed  Zeros 

Distinct70
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.9976756273
Minimum0
Maximum25000
Zeros68975
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:38.909393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum25000
Range25000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation109.7010304
Coefficient of variation (CV)109.9566105
Kurtosis40237.4155
Mean0.9976756273
Median Absolute Deviation (MAD)0
Skewness187.8503009
Sum69105
Variance12034.31607
MonotonicityNot monotonic
2025-05-27T21:31:39.451797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68975
99.0%
1 31
 
< 0.1%
2 24
 
< 0.1%
20 22
 
< 0.1%
10 19
 
< 0.1%
4 17
 
< 0.1%
30 16
 
< 0.1%
3 13
 
< 0.1%
8 11
 
< 0.1%
6 11
 
< 0.1%
Other values (60) 127
 
0.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68975
99.0%
1 31
 
< 0.1%
2 24
 
< 0.1%
3 13
 
< 0.1%
4 17
 
< 0.1%
ValueCountFrequency (%)
25000 1
< 0.1%
10000 1
< 0.1%
6000 2
< 0.1%
4500 1
< 0.1%
2400 2
< 0.1%

TOT_CONSTRUCCION.1
Real number (ℝ)

Skewed  Zeros 

Distinct1430
Distinct (%)2.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean279.0064534
Minimum0
Maximum360000
Zeros65972
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:39.931855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum360000
Range360000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4742.551285
Coefficient of variation (CV)16.99799853
Kurtosis1889.615957
Mean279.0064534
Median Absolute Deviation (MAD)0
Skewness37.62191625
Sum19325661
Variance22491792.69
MonotonicityNot monotonic
2025-05-27T21:31:40.412452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65972
94.7%
60 44
 
0.1%
200 41
 
0.1%
50 39
 
0.1%
20 36
 
0.1%
30 36
 
0.1%
80 35
 
0.1%
8 34
 
< 0.1%
6 32
 
< 0.1%
120 32
 
< 0.1%
Other values (1420) 2965
 
4.3%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 65972
94.7%
1 26
 
< 0.1%
2 14
 
< 0.1%
3 16
 
< 0.1%
4 23
 
< 0.1%
ValueCountFrequency (%)
360000 1
< 0.1%
317278 1
< 0.1%
309140 1
< 0.1%
252876 1
< 0.1%
244464 1
< 0.1%

TOT_NAUTICA
Real number (ℝ)

Skewed  Zeros 

Distinct117
Distinct (%)0.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1.713279242
Minimum0
Maximum22510
Zeros68833
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:41.083025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22510
Range22510
Interquartile range (IQR)0

Descriptive statistics

Standard deviation119.1193687
Coefficient of variation (CV)69.52711839
Kurtosis22971.66756
Mean1.713279242
Median Absolute Deviation (MAD)0
Skewness139.8332653
Sum118672
Variance14189.42401
MonotonicityNot monotonic
2025-05-27T21:31:41.571680image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68833
98.8%
1 35
 
0.1%
2 27
 
< 0.1%
3 25
 
< 0.1%
4 23
 
< 0.1%
10 22
 
< 0.1%
6 20
 
< 0.1%
5 20
 
< 0.1%
8 14
 
< 0.1%
60 13
 
< 0.1%
Other values (107) 234
 
0.3%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68833
98.8%
1 35
 
0.1%
2 27
 
< 0.1%
3 25
 
< 0.1%
4 23
 
< 0.1%
ValueCountFrequency (%)
22510 1
< 0.1%
15128 1
< 0.1%
9354 1
< 0.1%
6880 1
< 0.1%
5480 1
< 0.1%

TOT_PINTURAS
Real number (ℝ)

Skewed  Zeros 

Distinct549
Distinct (%)0.8%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean70.61439956
Minimum0
Maximum4340000
Zeros66380
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:42.005174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4340000
Range4340000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16491.13393
Coefficient of variation (CV)233.5378341
Kurtosis69252.19553
Mean70.61439956
Median Absolute Deviation (MAD)0
Skewness263.1450999
Sum4891177
Variance271957498.2
MonotonicityNot monotonic
2025-05-27T21:31:42.470574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66380
95.3%
10 119
 
0.2%
5 110
 
0.2%
2 100
 
0.1%
20 99
 
0.1%
30 79
 
0.1%
4 64
 
0.1%
50 57
 
0.1%
1 53
 
0.1%
8 52
 
0.1%
Other values (539) 2153
 
3.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 66380
95.3%
1 53
 
0.1%
2 100
 
0.1%
3 50
 
0.1%
4 64
 
0.1%
ValueCountFrequency (%)
4340000 1
< 0.1%
28121 1
< 0.1%
11524 1
< 0.1%
11050 1
< 0.1%
9092 1
< 0.1%

TOT_PROD_MEDICOS
Real number (ℝ)

Skewed  Zeros 

Distinct2248
Distinct (%)3.2%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean45946.34536
Minimum0
Maximum650697097
Zeros63179
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:42.923536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile70
Maximum650697097
Range650697097
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3830601.495
Coefficient of variation (CV)83.37119012
Kurtosis22006.15063
Mean45946.34536
Median Absolute Deviation (MAD)0
Skewness141.1744252
Sum3182519558
Variance1.467350781 × 1013
MonotonicityNot monotonic
2025-05-27T21:31:43.414340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63179
90.7%
1 322
 
0.5%
2 207
 
0.3%
10 180
 
0.3%
5 160
 
0.2%
3 149
 
0.2%
4 134
 
0.2%
20 121
 
0.2%
6 92
 
0.1%
30 88
 
0.1%
Other values (2238) 4634
 
6.6%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 63179
90.7%
1 322
 
0.5%
2 207
 
0.3%
3 149
 
0.2%
4 134
 
0.2%
ValueCountFrequency (%)
650697097 1
< 0.1%
607177029 1
< 0.1%
291024000 1
< 0.1%
270136939 1
< 0.1%
123526000 1
< 0.1%

TOT_PROD_VETERINARIOS
Real number (ℝ)

Skewed  Zeros 

Distinct182
Distinct (%)0.3%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean249.7811336
Minimum0
Maximum3180000
Zeros68901
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:44.037855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3180000
Range3180000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21934.21853
Coefficient of variation (CV)87.81375203
Kurtosis12781.87759
Mean249.7811336
Median Absolute Deviation (MAD)0
Skewness107.7967211
Sum17301340
Variance481109942.4
MonotonicityNot monotonic
2025-05-27T21:31:44.784341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68901
98.9%
2 22
 
< 0.1%
10 16
 
< 0.1%
1 16
 
< 0.1%
4 13
 
< 0.1%
200 10
 
< 0.1%
8 9
 
< 0.1%
3 9
 
< 0.1%
100 9
 
< 0.1%
5 9
 
< 0.1%
Other values (172) 252
 
0.4%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68901
98.9%
1 16
 
< 0.1%
2 22
 
< 0.1%
3 9
 
< 0.1%
4 13
 
< 0.1%
ValueCountFrequency (%)
3180000 1
 
< 0.1%
2500000 2
< 0.1%
2000000 1
 
< 0.1%
1450000 1
 
< 0.1%
1000000 4
< 0.1%

TOT_QUIMICOS
Real number (ℝ)

Skewed  Zeros 

Distinct1057
Distinct (%)1.5%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1634.667745
Minimum0
Maximum17000000
Zeros64593
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:45.244917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum17000000
Range17000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation85607.48172
Coefficient of variation (CV)52.36995836
Kurtosis23669.80766
Mean1634.667745
Median Absolute Deviation (MAD)0
Skewness133.8360939
Sum113226896
Variance7328640926
MonotonicityNot monotonic
2025-05-27T21:31:45.728873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64593
92.7%
2 184
 
0.3%
1 182
 
0.3%
10 171
 
0.2%
5 131
 
0.2%
4 117
 
0.2%
20 117
 
0.2%
3 116
 
0.2%
30 95
 
0.1%
100 84
 
0.1%
Other values (1047) 3476
 
5.0%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 64593
92.7%
1 182
 
0.3%
2 184
 
0.3%
3 116
 
0.2%
4 117
 
0.2%
ValueCountFrequency (%)
17000000 1
< 0.1%
6500000 1
< 0.1%
6000000 1
< 0.1%
5302471 1
< 0.1%
3446436 1
< 0.1%

TOT_REPUESTOS
Real number (ℝ)

Skewed  Zeros 

Distinct1028
Distinct (%)1.5%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean436.5953426
Minimum0
Maximum9500000
Zeros60184
Zeros (%)86.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:46.283322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile64
Maximum9500000
Range9500000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46070.95505
Coefficient of variation (CV)105.5232399
Kurtosis28540.51158
Mean436.5953426
Median Absolute Deviation (MAD)0
Skewness156.7298713
Sum30241213
Variance2122532900
MonotonicityNot monotonic
2025-05-27T21:31:46.712699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60184
86.4%
1 625
 
0.9%
2 404
 
0.6%
4 307
 
0.4%
10 268
 
0.4%
3 236
 
0.3%
5 229
 
0.3%
6 228
 
0.3%
20 217
 
0.3%
8 188
 
0.3%
Other values (1018) 6380
 
9.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 60184
86.4%
1 625
 
0.9%
2 404
 
0.6%
3 236
 
0.3%
4 307
 
0.4%
ValueCountFrequency (%)
9500000 1
< 0.1%
4000000 1
< 0.1%
3850000 1
< 0.1%
3780000 1
< 0.1%
3000000 1
< 0.1%

TOT_PLOMERIA
Real number (ℝ)

Skewed  Zeros 

Distinct493
Distinct (%)0.7%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean13.2097999
Minimum0
Maximum188500
Zeros67287
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:47.086744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum188500
Range188500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1044.116505
Coefficient of variation (CV)79.04105381
Kurtosis30437.39011
Mean13.2097999
Median Absolute Deviation (MAD)0
Skewness170.6935309
Sum914990
Variance1090179.276
MonotonicityNot monotonic
2025-05-27T21:31:47.580771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67287
96.6%
1 116
 
0.2%
2 82
 
0.1%
4 80
 
0.1%
10 68
 
0.1%
20 55
 
0.1%
3 49
 
0.1%
5 46
 
0.1%
6 39
 
0.1%
12 34
 
< 0.1%
Other values (483) 1410
 
2.0%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67287
96.6%
1 116
 
0.2%
2 82
 
0.1%
3 49
 
0.1%
4 80
 
0.1%
ValueCountFrequency (%)
188500 1
< 0.1%
187500 1
< 0.1%
52000 1
< 0.1%
22292 1
< 0.1%
19872 1
< 0.1%

TOT_SERV_PROFESIONAL
Real number (ℝ)

Skewed  Zeros 

Distinct1084
Distinct (%)1.6%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean36139.73839
Minimum0
Maximum130000000
Zeros56623
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:48.122895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile24
Maximum130000000
Range130000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1527052.354
Coefficient of variation (CV)42.25410649
Kurtosis4219.863548
Mean36139.73839
Median Absolute Deviation (MAD)0
Skewness61.1866789
Sum2503255119
Variance2.331888893 × 1012
MonotonicityNot monotonic
2025-05-27T21:31:48.644312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56623
81.3%
1 5129
 
7.4%
12 1356
 
1.9%
2 687
 
1.0%
3 385
 
0.6%
24 303
 
0.4%
6 270
 
0.4%
4 251
 
0.4%
5 166
 
0.2%
36 123
 
0.2%
Other values (1074) 3973
 
5.7%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 56623
81.3%
1 5129
 
7.4%
2 687
 
1.0%
3 385
 
0.6%
4 251
 
0.4%
ValueCountFrequency (%)
130000000 1
 
< 0.1%
120000000 4
< 0.1%
100000000 1
 
< 0.1%
90000000 4
< 0.1%
75000000 1
 
< 0.1%

TOT_SERV_NOTICIAS
Real number (ℝ)

Skewed  Zeros 

Distinct17
Distinct (%)< 0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean122.974634
Minimum0
Maximum4250000
Zeros69000
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:49.178934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4250000
Range4250000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22837.12543
Coefficient of variation (CV)185.7059841
Kurtosis34630.26856
Mean122.974634
Median Absolute Deviation (MAD)0
Skewness186.0944746
Sum8517961
Variance521534298
MonotonicityNot monotonic
2025-05-27T21:31:49.511472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 69000
99.0%
12 195
 
0.3%
1 47
 
0.1%
24 6
 
< 0.1%
48 3
 
< 0.1%
49 2
 
< 0.1%
2 2
 
< 0.1%
4250000 2
 
< 0.1%
4499 1
 
< 0.1%
5 1
 
< 0.1%
Other values (7) 7
 
< 0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69000
99.0%
1 47
 
0.1%
2 2
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
4250000 2
< 0.1%
10000 1
< 0.1%
4499 1
< 0.1%
600 1
< 0.1%
49 2
< 0.1%

TOT_SERV_BASICOS
Real number (ℝ)

Skewed  Zeros 

Distinct274
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5622.205339
Minimum0
Maximum144000000
Zeros67930
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:49.945742image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum144000000
Range144000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation643045.7006
Coefficient of variation (CV)114.376061
Kurtosis37964.32417
Mean5622.205339
Median Absolute Deviation (MAD)0
Skewness182.6082737
Sum389427675
Variance4.135077731 × 1011
MonotonicityNot monotonic
2025-05-27T21:31:50.459960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67930
97.5%
1 307
 
0.4%
12 286
 
0.4%
24 106
 
0.2%
2 37
 
0.1%
13 33
 
< 0.1%
3 28
 
< 0.1%
48 26
 
< 0.1%
6 23
 
< 0.1%
36 18
 
< 0.1%
Other values (264) 472
 
0.7%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 67930
97.5%
1 307
 
0.4%
2 37
 
0.1%
3 28
 
< 0.1%
4 17
 
< 0.1%
ValueCountFrequency (%)
144000000 1
< 0.1%
60000000 1
< 0.1%
46993309 1
< 0.1%
36000000 1
< 0.1%
21000000 1
< 0.1%

TOT_TAPICERIA
Real number (ℝ)

Skewed  Zeros 

Distinct70
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.1551843617
Minimum0
Maximum1300
Zeros69080
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:50.939509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1300
Range1300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.574523442
Coefficient of variation (CV)48.80983726
Kurtosis15684.01471
Mean0.1551843617
Median Absolute Deviation (MAD)0
Skewness109.6085885
Sum10749
Variance57.37340537
MonotonicityNot monotonic
2025-05-27T21:31:51.518671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69080
99.1%
1 19
 
< 0.1%
10 18
 
< 0.1%
5 13
 
< 0.1%
4 10
 
< 0.1%
2 8
 
< 0.1%
6 6
 
< 0.1%
20 6
 
< 0.1%
30 6
 
< 0.1%
3 6
 
< 0.1%
Other values (60) 94
 
0.1%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69080
99.1%
1 19
 
< 0.1%
2 8
 
< 0.1%
3 6
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
1300 1
< 0.1%
845 1
< 0.1%
600 1
< 0.1%
400 2
< 0.1%
280 2
< 0.1%

TOT_TRANSPORTE
Real number (ℝ)

Skewed  Zeros 

Distinct273
Distinct (%)0.4%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean847.056911
Minimum0
Maximum8550164
Zeros68091
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:51.993465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8550164
Range8550164
Interquartile range (IQR)0

Descriptive statistics

Standard deviation67105.40859
Coefficient of variation (CV)79.22184179
Kurtosis12101.52753
Mean847.056911
Median Absolute Deviation (MAD)0
Skewness107.282094
Sum58672244
Variance4503135862
MonotonicityNot monotonic
2025-05-27T21:31:52.510048image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68091
97.7%
1 221
 
0.3%
2 100
 
0.1%
12 75
 
0.1%
3 50
 
0.1%
4 48
 
0.1%
20 45
 
0.1%
6 33
 
< 0.1%
8 30
 
< 0.1%
10 29
 
< 0.1%
Other values (263) 544
 
0.8%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68091
97.7%
1 221
 
0.3%
2 100
 
0.1%
3 50
 
0.1%
4 48
 
0.1%
ValueCountFrequency (%)
8550164 1
< 0.1%
8117736 1
< 0.1%
7704915 1
< 0.1%
6971804 1
< 0.1%
5276288 1
< 0.1%

TOT_PROD_DEPORTIVOS
Real number (ℝ)

Skewed  Zeros 

Distinct88
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean7.407689198
Minimum0
Maximum200000
Zeros69097
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:53.026258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum200000
Range200000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation870.0280584
Coefficient of variation (CV)117.4493199
Kurtosis41207.24122
Mean7.407689198
Median Absolute Deviation (MAD)0
Skewness189.3948061
Sum513101
Variance756948.8224
MonotonicityNot monotonic
2025-05-27T21:31:53.558413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69097
99.2%
4 12
 
< 0.1%
5 9
 
< 0.1%
2 8
 
< 0.1%
20 7
 
< 0.1%
50 7
 
< 0.1%
16 5
 
< 0.1%
8 5
 
< 0.1%
1 5
 
< 0.1%
12 4
 
< 0.1%
Other values (78) 107
 
0.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 69097
99.2%
1 5
 
< 0.1%
2 8
 
< 0.1%
3 2
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
200000 1
< 0.1%
62731 1
< 0.1%
56000 1
< 0.1%
54400 1
< 0.1%
32473 1
< 0.1%

TOT_VIDRIERIA
Real number (ℝ)

Skewed  Zeros 

Distinct59
Distinct (%)0.1%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.1761759016
Minimum0
Maximum2925
Zeros68963
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:54.070147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2925
Range2925
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.49624624
Coefficient of variation (CV)70.93050825
Kurtosis43711.31746
Mean0.1761759016
Median Absolute Deviation (MAD)0
Skewness193.3228802
Sum12203
Variance156.1561702
MonotonicityNot monotonic
2025-05-27T21:31:54.644400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68963
99.0%
2 51
 
0.1%
1 33
 
< 0.1%
10 25
 
< 0.1%
4 22
 
< 0.1%
3 18
 
< 0.1%
5 11
 
< 0.1%
20 11
 
< 0.1%
15 10
 
< 0.1%
6 9
 
< 0.1%
Other values (49) 113
 
0.2%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68963
99.0%
1 33
 
< 0.1%
2 51
 
0.1%
3 18
 
< 0.1%
4 22
 
< 0.1%
ValueCountFrequency (%)
2925 1
< 0.1%
667 2
< 0.1%
600 1
< 0.1%
512 1
< 0.1%
450 1
< 0.1%

TOT_VIGILANCIA
Real number (ℝ)

Skewed  Zeros 

Distinct222
Distinct (%)0.3%
Missing420
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean76.39933012
Minimum0
Maximum999360
Zeros68552
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:55.790646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum999360
Range999360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4747.786078
Coefficient of variation (CV)62.14434172
Kurtosis30445.16139
Mean76.39933012
Median Absolute Deviation (MAD)0
Skewness159.2243653
Sum5291876
Variance22541472.64
MonotonicityNot monotonic
2025-05-27T21:31:56.274755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68552
98.4%
12 152
 
0.2%
1 139
 
0.2%
6 28
 
< 0.1%
2 19
 
< 0.1%
3 16
 
< 0.1%
24 13
 
< 0.1%
4 12
 
< 0.1%
8 10
 
< 0.1%
5 9
 
< 0.1%
Other values (212) 316
 
0.5%
(Missing) 420
 
0.6%
ValueCountFrequency (%)
0 68552
98.4%
1 139
 
0.2%
2 19
 
< 0.1%
3 16
 
< 0.1%
4 12
 
< 0.1%
ValueCountFrequency (%)
999360 1
< 0.1%
467253 1
< 0.1%
393360 1
< 0.1%
206560 1
< 0.1%
128592 1
< 0.1%

P_monto_total_procesos
Real number (ℝ)

Missing  Skewed 

Distinct51353
Distinct (%)94.9%
Missing15561
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean13939301.15
Minimum0
Maximum1.6438422 × 1010
Zeros472
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:56.674403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25566.54585
Q1234308.5714
median930952.381
Q34117933.29
95-th percentile41254081.71
Maximum1.6438422 × 1010
Range1.6438422 × 1010
Interquartile range (IQR)3883624.719

Descriptive statistics

Standard deviation143734577.9
Coefficient of variation (CV)10.31146227
Kurtosis4409.322402
Mean13939301.15
Median Absolute Deviation (MAD)851971.2999
Skewness53.20733228
Sum7.544646747 × 1011
Variance2.065962888 × 1016
MonotonicityNot monotonic
2025-05-27T21:31:57.044612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 472
 
0.7%
255000 17
 
< 0.1%
510000 15
 
< 0.1%
612000 14
 
< 0.1%
1224000 12
 
< 0.1%
191250 11
 
< 0.1%
10200000 10
 
< 0.1%
306000 10
 
< 0.1%
102000 10
 
< 0.1%
382500 10
 
< 0.1%
Other values (51343) 53544
76.8%
(Missing) 15561
 
22.3%
ValueCountFrequency (%)
0 472
0.7%
0.01 1
 
< 0.1%
0.01085106383 1
 
< 0.1%
0.01378378378 1
 
< 0.1%
0.01457142857 1
 
< 0.1%
ValueCountFrequency (%)
1.6438422 × 10101
< 0.1%
1.084790825 × 10101
< 0.1%
8100236867 1
< 0.1%
7403225806 1
< 0.1%
6458785714 1
< 0.1%

P_cant_proveedores
Real number (ℝ)

Missing 

Distinct27
Distinct (%)< 0.1%
Missing15561
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean1.99334873
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size544.5 KiB
2025-05-27T21:31:57.590737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum31
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.842415324
Coefficient of variation (CV)0.9242814851
Kurtosis19.07482135
Mean1.99334873
Median Absolute Deviation (MAD)0
Skewness3.421344949
Sum107890
Variance3.394494227
MonotonicityNot monotonic
2025-05-27T21:31:57.991476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 31905
45.8%
2 10881
 
15.6%
3 4118
 
5.9%
4 2743
 
3.9%
5 1565
 
2.2%
6 1093
 
1.6%
7 601
 
0.9%
8 440
 
0.6%
9 244
 
0.4%
10 196
 
0.3%
Other values (17) 339
 
0.5%
(Missing) 15561
22.3%
ValueCountFrequency (%)
1 31905
45.8%
2 10881
 
15.6%
3 4118
 
5.9%
4 2743
 
3.9%
5 1565
 
2.2%
ValueCountFrequency (%)
31 2
< 0.1%
28 2
< 0.1%
25 1
< 0.1%
24 2
< 0.1%
23 2
< 0.1%